AWS for Software Companies Podcast
- Folgen
- 232
- Erscheint
- wöchentlich
- Letzte Folge
- 12. Nov. 2026
Über den Podcast
Stay ahead of the rapidly evolving cloud and AI landscape with the AWS for Software Companies podcast.
Hear from renowned software leaders, respected industry analysts, and experienced consultants alongside AWS experts as they explore the technologies shaping the future—from generative AI and agentic systems to intelligent cloud architectures, and modern data management. Learn how AI agents are transforming enterprise workflows, how leading companies are modernizing their cloud strategies with security best practices at the core, and what's driving the next wave of SaaS innovation.
New episodes drop regularly to keep you informed on the trends that matter most to your business.
Folgen 232
Ep232: From On-Prem to AI-Native: Nexthink’s Big Bet on Autonomous IT
Nexthink’s journey from on-prem to cloud to AI-native shows how two decades of endpoint data, Amazon Bedrock and rigorous evaluations power Spark, an autonomous agent that is reshaping the service desk.Topics …
Shownotes
Nexthink’s journey from on-prem to cloud to AI-native shows how two decades of endpoint data, Amazon Bedrock and rigorous evaluations power Spark, an autonomous agent that is reshaping the service desk.
Topics Include:
- Nexthink: two decades turning workplace IT failures into fixable signals.
- Monitoring 25 million endpoints, processing gigabytes of real-time signal per second.
- AI-native revenue jumped from $20M to $109M in one year.
- Rewriting every line of code to go from on-prem to cloud.
- Why cloud transformation is about culture and mindset, not just technology.
- Remediation and workflow engines running billions of fixes at scale.
- How ChatGPT's arrival sparked an AI lab to reinvent the company.
- Six months of prototyping that became an autonomous IT agent.
- Every enterprise shares one lasting problem: technology will eventually fail.
- Employees suffer first, work around issues, and rarely report them.
- Giving IT visibility and tools to fix problems before tickets pile up.
- Why data and context, not frontier models, are the real moat.
- "Teams is slow": what generic AI guesses versus what telemetry knows.
- Why everyone hated chatbots: they routed tickets instead of solving problems.
- Spark's success metric: resolve in minutes, escalate only when necessary.
- Grounded in device data, Spark knows more than you tell it.
- Eyes on the problem, hands to fix it, with permissions respected.
- A hotel-room VPN failure diagnosed from a single screenshot.
- Inside troubleshooting mode: hypotheses, diagnostics, and an audited fix in seconds.
- Why Amazon Bedrock enabled a model-agnostic, multi-model strategy.
- When to stick with foundation models and when to fine-tune.
- The harness: where most agent engineering time actually goes.
- Evaluations from day zero: never ship changes on intuition.
- "Let the LLM reason, let the data decide."
- Treating AWS as an engineering partner, not a supplier.
- Scaling Amazon MSK from 5 to roughly 15 gigabytes per second.
- Will the service desk as we know it survive five years?
- Customers reporting 70% resolution rates and 40% ticket deflection.
- A broken-monitor fix that uncovered hundreds of employees with the same unreported problem.
- An email distribution list request handled by Spark in minutes.
Participants:
- Moe Haidar: Head of Agentic AI Engineering (Spark AI), Nexthink
- Kamil Davidov: Sales Leader, Israel ISV-BizApps, Amazon Web Services
See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Ep231: Lead with AI on AWS
See how enterprises run AI in production on AWS, from frontier models on Amazon Bedrock to Amazon Bedrock AgentCore for governing, securing and tracing agents across every systemTopics Include:Why software …
Shownotes
See how enterprises run AI in production on AWS, from frontier models on Amazon Bedrock to Amazon Bedrock AgentCore for governing, securing and tracing agents across every system
Topics Include:
- Why software breaks into five layers: persona, interface, logic, platform, data.
- AI is changing who uses software and how they interact with it.
- Workflows are breaking out of single SaaS tools and running across multiple providers.
- Zero-copy data access unlocks insights trapped in separate departments.
- One AI surface replaces three logins across Salesforce, SAP and Adobe.
- Governing agents: trusted registries, cost controls and token management.
- Security has to run at machine speed when vulnerabilities surface daily.
- Tracing and evaluating workflows that hop between different systems.
- Margin compression: what token costs do to the economics of AI products.
- Four pricing models, and why seats-plus-tokens hybrids are winning.
- Embed, extend, expand or create: a two-by-two grid for AI products.
- Real examples: Intercom Fin, Slack AI tiers, Workday HiredScore.
- First mover or fast follower? AI-built PR FAQs speed up product decisions.
- Why roughly 70% of developer time isn’t spent coding.
- MCP as the new TCP/IP for multi-vendor agent ecosystems.
- Why non-technical salespeople are quickly embracing AI interfaces.
- The four layers: AI surface, harness, reasoning models and chips.
- Why context and memory in the harness make or break agents.
- Building for security, scale and open standards across the AI stack.
- Will models eat the harness? Data and context take centre stage.
Participants:
- Mahdi Sajjadpour – Director, Solutions Architecture, Strategic ISVs – Amazon Web Services
See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Ep230: AI First or Human First - Front’s CEO on the Future of Customer Experience
Front CEO Dan O’Connell explores whether customer experience should be AI-first or human-first, and why unifying siloed teams onto a single platform transforms the customer experience.Topics Include:How …
Shownotes
Front CEO Dan O’Connell explores whether customer experience should be AI-first or human-first, and why unifying siloed teams onto a single platform transforms the customer experience.
Topics Include:
- How customer-facing teams manage complex relationships across email, chat and voice
- From headsets and green screens to today’s modern customer service
- The defining question: AI-first or human-first customer touch points?
- Why siloed support, success and sales tools fragment the customer experience
- Automation versus experience: deciding what your customers actually want
- Why experimenting with AI now protects you from faster competitors
- Customer support’s clear metrics make it prime territory for AI ROI
- How a shared inbox idea evolved to suit an agentic world
- Unifying support, account management and customer success in one place
- A logistics workflow turning plain-text emails into app-like quote comparisons
- Seamless handoffs between AI agents and humans on a single platform
- Why email and voice are critical infrastructure where downtime costs revenue
- Scaling from startup credits to enterprise without a major rewrite
- Moving upmarket and why customers’ cloud commitments matter
- Bringing telephony into the unified thread via Amazon Connect
- Is voice dying? Generational and company-size differences explored
- Why AI adoption moves at different speeds outside Silicon Valley
- Choosing AI models: balancing quality, latency, price and innovation
- The ultimate test: not knowing whether a human or agent helped
- Spotting the warning signs: teams coordinating problems instead of solving them
Participants:
- Dan O’Connell – Chief Executive Officer, Front
- Matt Rivron – Account Manager, Amazon Web Services
See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Ep229: Safe Code at Agent Speed – LaunchDarkly’s Safety Net for AI-Built Software
LaunchDarkly CPO Jonathan Nolen breaks down how teams can build, validate, and ship software at agent speed, without losing control.Topics Include:Jonathan Nolen explains how LaunchDarkly helps 3,000 …
Shownotes
LaunchDarkly CPO Jonathan Nolen breaks down how teams can build, validate, and ship software at agent speed, without losing control.
Topics Include:
- Jonathan Nolen explains how LaunchDarkly helps 3,000 enterprises manage change.
- Target features down to individual users and toggle them without redeploying.
- Customers range from startups and government agencies to airlines and pizza delivery.
- Why LaunchDarkly sells cultural change, not just a developer tool.
- Engineers are creatures of habit, so adoption takes real evangelism.
- Instant rollback means you hear about incidents next morning, not at 2 a.m.
- We’re living through a rebirth of the software development lifecycle.
- Ramp is building its own software factory powered by autonomous agents.
- A healthcare insurer is building its agent factory on just three tools.
- Start agents on bug fixes, then features, then whole projects.
- Why Nolen is retiring the term “vibe coding.”
- Code written at agent speed must be validated at agent speed.
- There are two levers: invest in testing, or shrink the blast radius.
- Progressive rollouts move from internal users to synthetic tests to beta cohorts.
- No single safeguard is enough; layered techniques build real confidence.
- The key metric is valuable software shipped per unit of human attention.
- Automate everything, and bring humans in only where judgment matters.
- LaunchDarkly is on the same AI journey as its customers.
- Why LaunchDarkly sees itself as critical infrastructure, not just SaaS.
- It designs for failure, so customer apps keep running during outages.
- Two questions to ask: how fast, and how granularly, can you fix releases?
- Big-batch rollbacks hold every team captive to the slowest feature.
- Turn off just the broken feature, fleet-wide, in under a second.
- Nolen’s biggest recent lesson: first ask AI how to solve the problem.
- Inside LaunchDarkly’s culture: one team that wins or loses together.
- Ownership, smart bets, and using risk to your advantage.
- Radical transparency means less time spent agreeing on what reality is.
- Cheap code creates new bottlenecks, like humans trying to review everything.
- The future is learning by building and testing ideas directly in production.
- The organizations that close this loop fastest will see compounding advantages.
Participants:
- Jonathan Nolen – Chief Product Officer, LaunchDarkly
See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Ep228: Free to Freemium: How Veeva Transitioned While Retaining Trust
Discover how Veeva transformed a free clinical research platform into a sustainable freemium business, balancing with their mission through transparent communication, fair pricing, and smart migration tooling …
Shownotes
Discover how Veeva transformed a free clinical research platform into a sustainable freemium business, balancing with their mission through transparent communication, fair pricing, and smart migration tooling built on AWS.
Topics Include:
- How Veeva builds industry cloud software for clinical research sites
- Why clinical trials sit between healthcare delivery and life sciences
- How a free platform grew into a self-sustaining business
- Balancing monetization and mission: free tiers, fair pricing, annual true-ups
- Giving 15 months' notice and over-communicating the why
- Building migration tooling to move users from multi-tenant to enterprise
- Handling edge cases and migration issues through radical transparency
- Navigating customer pushback amid 2025 NIH funding cuts
- What a strategic AWS partnership looks like beyond infrastructure
- Getting more from AWS by sharing long-term goals, not transactions
- Advice for SaaS leaders: set boundaries, prioritize fairness, own the message
- Preparing teams for change without gaming out every objection
Participants:
- Nick Frenzer – General Manager, Site Solutions, Veeva
See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Ep227: How BlackBerry Is Migrating 1,500+ VMs to AWS in Canada
Go inside a massive, still-in-progress migration of 1,500+ servers out of two Canadian data centers, with candid lessons on what works, what was surprising, and what to do differently.Topics Include:Inside a …
Shownotes
Go inside a massive, still-in-progress migration of 1,500+ servers out of two Canadian data centers, with candid lessons on what works, what was surprising, and what to do differently.
Topics Include:
- Inside a live migration: 1,500+ servers leaving two Canadian data centers.
- How BlackBerry became a secure communications and QNX vehicle software company.
- Decades of legacy systems, and why standing still became costlier.
- Three converging forces: VMware licensing, hardware end-of-life, and FinOps gaps.
- The goals: close data centers, exit VMware, modernize only what matters.
- Choosing partners and funding programs that work as one team.
- A five-week assessment with parallel workstreams and five key workshops.
- Rehost, re-platform, or refactor: pragmatism over migration ideology.
- Early lessons on discovery, cost transparency, and over-communicating.
- Building the landing zone: accounts, networking, identity, and security.
- Discovery, right-sizing, then migrating, validating, and cutting over workloads.
- Wave planning: start simple, save the most complex workloads for last.
- Day two: cost tagging, observability, and AI-assisted modernization backlogs.
- Final advice: business case first, migrate now, modernize later.
Participants:
- Jason Whissell – Vice President, IT & Business Application Solutions, Blackberry
- Mehran Najafi PhD - Principal AI Architect & Head of AI Solution Architects, AWS Canada, Amazon Web Services
See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Ep226: The Intelligent Legal Work Platform - Clio's Journey on AWS
From data-center chaos to billion-dollar acquisitions: the real story behind Clio's cloud journey, compliance battles, and rapid AI-driven transformation.Topics Include:AWS-Clio partnership: migrate, modernize …
Shownotes
From data-center chaos to billion-dollar acquisitions: the real story behind Clio's cloud journey, compliance battles, and rapid AI-driven transformation.
Topics Include:
- AWS-Clio partnership: migrate, modernize, add AI, then co-sell
- Legal industry ran on WordPerfect, mainframes, and homemade billing
- Clio launched 2008, same year as AWS's first database
- Now serves 400,000+ legal professionals across 130 countries
- Landed legal tech's largest-ever acquisition, raised $500M Series G
- Ships code 50-100 times daily in a regulated industry
- Started split across a data center, Azure, AWS, some GCP
- Nine years of acquisitions, fundraising, and rapid team growth
- Sharded databases, added regions for compliance and low latency
- Engineering team grew from 40 to over 650 people
- Lawyers once feared cloud security and data ownership questions
- Three principles: data security, system resilience, data regionality
- Stage one: survival, learning EC2, RDS, S3 basics
- Stage two: earning trust through SOC 2, PCI compliance
- AWS became a compliance partner, not just a host
- Stage three: AI reshapes coding speed and company culture
- Integrating acquisitions scattered across Heroku, GCP, Azure, others
- VLUX deal added a billion-document legal case-law database
- Rapid acquisition-to-launch playbook: some products shipped in days
- Strategy: avoid single AI vendor, preserve maximum optionality
Participants:
- Jonathan Watson – Chief Technology Officer, Clio
- Martin Bazinet – Director of Technology, AWS Canada, Amazon Web Services
See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Ep225: From COBOL to Agile Workflows - Accelerate Mainframe Modernization with AWS Transform and Pega Blueprint
Experts from Pegasystems and AWS reveal how AI-powered tools now modernize decades-old mainframe systems in weeks instead of years—unlocking trapped data faster than ever.Topics Include:Session covers …
Shownotes
Experts from Pegasystems and AWS reveal how AI-powered tools now modernize decades-old mainframe systems in weeks instead of years—unlocking trapped data faster than ever.
Topics Include:
- Session covers accelerating mainframe modernization using AWS Transform and Pega Blueprint
- Agenda: challenges, joint solution, value prop, customer story, rollout mechanism
- Mainframe costs rise 20-30% yearly, straining IT budgets significantly
- Monolithic systems create long time-to-market, hurting competitiveness against newer rivals
- Mainframe data stays locked away, blocking AI adoption and innovation
- Retiring mainframe skills outpace new workforce training, compounding the problem
- AI now enables analyzing decades-old code bases within weeks, not years
- AWS Transform extracts business logic; Pega Blueprint visualizes modernized target applications
- Five-step journey: Transform, Pega Foundry, Blueprint, then deployment via Infinity platform
- Three Transform agents handle code, data, and activity analysis separately
- Data analysis covers data lineage and data dictionary schema extraction
- Outputs feed a knowledge graph, enabling natural language queries anytime
- Transform deterministically identifies distinct data paths from mainframe systems
- Data paths get grouped into business functions, each modernizable independently
- Final steps extract business logic and generate plain-English requirements
- Blueprint agent enriches Transform outputs, enabling intuitive workflow design
- Blueprint offers runtime previews and is publicly accessible at pega.com
- Pega Infinity platform handles both batch and online mainframe workloads
- Insurance company Unum modernized 1.5M lines of COBOL in three months
- Experience-Based Acceleration program delivers modernized, working code within four-to-six weeks
Participants:
- Surender Kumar – Director, Mainframe Modernization, Pegasystems
- Ujwal Bukka – Sr Partner Solutions Architect, Business Apps, Amazon Web Services
- Sourav Sarkar – Sr Worldwide Specialist Solutions Architect, Mainframe & Legacy Modernization, Amazon Web Services
See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Ep224: Billing the Unbillable: How Zuora Monetizes AI on Amazon AgentCore
Discover how Zuora turned its own billing platform inward to track AI costs down to the individual engineer, giving CFOs the trust and auditability they demand.Topics Include:AWS's Achint Naveen opens …
Shownotes
Discover how Zuora turned its own billing platform inward to track AI costs down to the individual engineer, giving CFOs the trust and auditability they demand.
Topics Include:
- AWS's Achint Naveen opens, introduces Zuora as a key partner
- Audience poll reveals AI monetization is still a struggle
- Zuora's Katherine Shealy introduces the company's AI monetization focus
- Zuora powers monetization for global brands like Twilio, Zoom
- Their platform spans CPQ, billing, metering, and revenue recognition
- Usage-based pricing has flipped seat-based SaaS models upside down
- AI companies face a 20-point margin gap versus SaaS
- Companies juggling multiple pricing models face major hidden complexity
- Traditional pricing models are breaking down under AI's dynamic costs
- Zuora's framework aligns teams early through an NPI-style process
- Their tech lets companies iterate pricing at engineering speed
- Zuora now tracks its own AI costs internally too
- Zuora AI runs on AWS Bedrock and AgentCore infrastructure
- AWS's Achint details the full stack behind Zuora AI
- AgentCore Memory gives Zuora's agents secure, persistent context
Participants:
- Katherine Shealy – Principal Product Marketing Manager, Zuora
- Achint Naveen – Sr Account Manager, Amazon Web Services
See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Ep223: Cybersecurity in the Age of AI – Inside Sophos, the World’s Largest Agentic SOC
Sophos CSO Simon Reed reveals how attackers now operate like leveraged businesses—and how AI is making both sides of the cyber arms race dramatically more sophisticated.Topics Include:Intro: Simon Reed, CSO at …
Shownotes
Sophos CSO Simon Reed reveals how attackers now operate like leveraged businesses—and how AI is making both sides of the cyber arms race dramatically more sophisticated.
Topics Include:
- Intro: Simon Reed, CSO at Sophos, joins Kamil Davidov.
- Sophos defends 625,000+ organizations with AI-native Fusion platform.
- Sophos MDR runs the world's largest agentic SOC.
- Simon's journey: cybersecurity since 2001, from graffiti to warfare.
- Cyber threats framed as constant toxicity, not occasional lightning strikes.
- Sophos spans small business to enterprise, giving unique threat visibility.
- Attackers now operate as leveraged, ecosystem-driven businesses, not lone actors.
- AI dramatically increases attack quality, speed, and sophistication.
- AWS Local Zones cut latency for Sophos's threat intelligence cloud.
- IntellX connects every product to real-time global threat data.
- "Good day" means finding novelty before attackers exploit it.
- Worst days: sudden, unpredictable supply chain compromise scenarios.
- Sophos updates protection across 600,000 customers within minutes.
- Decisions made fast, on incomplete data, balancing speed and risk.
- Sophos has pursued AI and deep learning for eight-plus years.
- Agentic AI's next phase reduces laborious human security work.
- Simon prioritizes class-based, automation-scale defenses over budgets.
- Sophos cut data platform costs 30%, migrated ahead of schedule.
- Advice: hyper-engage AWS account teams, push for more.
- Closing story: Sophos helped stop ransomware hitting hospitals during COVID.
Participants:
- Simon Reed – Chief Security Officer, Sophos
- Kamil Davidov – Sales Leader Israel ISV-BizApps, Amazon Web Services
See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Ep222: Vibe Coding at Scale – How Retool is Securing the AI Software Boom
Vibe coding is accelerating code development within the Enterprise, but at what cost of security? Retool’s CEO shares how you can accelerate software development while managing the risks effectively.Topics …
Shownotes
Vibe coding is accelerating code development within the Enterprise, but at what cost of security? Retool’s CEO shares how you can accelerate software development while managing the risks effectively.
Topics Include:
- Internal software runs pharmacies, grocery checkouts, and airport check-ins daily
- Retool manages and de-risks environment from insecure code
- AI has driven a 10x surge in code volume since 2025
- More code plus less human review means falling code quality
- Non-engineers can now build working apps in minutes flat
- But deploying and securing those apps remains genuinely difficult
- Multiple CIOs found private company data exposed on the internet
- Uncontrolled model choice is quietly driving up company costs
- Attackers now deploy automated agent swarms, not lone hackers
- Defense is asymmetric: one flaw in, everything must hold
- Retool runs inside customers' own cloud, often on AWS
- A single data choke point enables logging, audits, authorization
- Guardrails, not their absence, actually let teams move faster
- Security audits reveal major gaps in nearly all companies checked
- Nearly all companies may face a serious hack within 18 months
Participants:
See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Ep221: The Agentic Inflection Point with Couchbase, SentinelOne and AWS
In a fascinating panel discussion, executives from Couchbase, SentinelOne and AWS share strategies ensuring the highest adoption and ROI when deploying AI and the pitfalls to avoid along the way.Topics …
Shownotes
In a fascinating panel discussion, executives from Couchbase, SentinelOne and AWS share strategies ensuring the highest adoption and ROI when deploying AI and the pitfalls to avoid along the way.
Topics Include:
- Agentic AI marks shift from clever data to autonomous agents
- Organizations sit at different AI maturity levels, not uniform
- Only 31% of adopters see measurable financial impact from AI
- Adoption friction, not access, is what stalls most AI value
- Segment your workforce: innovators, pragmatic majority, and reluctant laggards
- Give innovators tools, budget, and freedom before chasing laggards
- Visible recognition programs turn early innovators into internal role models
- AI-ready data infrastructure remains the top blocker to real adoption
- Future AI value concentrates at customer-facing, transaction-level touchpoints
- Coding tools see fast uptake; broader business adoption lags behind
- AI is quietly rewriting SEO, SEM, and lead-routing strategy
- Agent trust requires both clean data and strong safety guardrails
- Human-in-the-loop review lets AI handle investigation, humans decide outcomes
- In agentic AI, go-to-market partnerships matter more than past tech waves
- Strong tech-platform partnerships help smaller vendors punch above their weight
- Marketplace listings can cut sales cycles from months to weeks
- Best partnerships form around solving hard problems, not quarterly quotas
- Confidence gap: only 38% of employees feel AI-ready
Participants:
- Deirdre Toner – President & Chief Commercial Officer, Couchbase
- Eran Ashkenazi - Chief Business Officer, SentinelOne
- Connie de Lange – Director, AWS Strategic Customer & Partner Marketing, North America, Amazon Web Services
- Matt Wood – Chief AI & Technology Officer, Amazon Web Services
See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Ep220: The AI Architecture We Deleted - featuring Demandbase
Demandbase's Vice President of Product explains why they deleted a fully-approved AI architecture two months before launch — and how the rebuild surpassed some of their customer’s highest expectations.Topics …
Shownotes
Demandbase's Vice President of Product explains why they deleted a fully-approved AI architecture two months before launch — and how the rebuild surpassed some of their customer’s highest expectations.
Topics Include:
- AWS's Achint Naveen introduces Demandbase's VP of Product, Chad Holdorf
- Demandbase unifies sales, marketing, and revenue data into one view
- November's architecture used many specialized agents, all committee-approved
- That design failed constantly — only a 30% conversation pass rate
- On May 11th, the team deleted the entire architecture
- Rebuilt in May with AWS Strands: one simpler, flexible agent
- Pass rate leapt from 30% to 94% almost overnight
- Week two retention rose from the low 20s to upper 80s
- Weekly active users grew 45% week-over-week after launch
- Real customer interviews play, calling the new AI a "dream"
- One user cut an hour-long report down to fifteen minutes
- Customers now trace ad impressions directly to closed deals
- Holdorf's advice: delete and rebuild when architecture gets too complex
- AWS's Naveen walks through Bedrock, AgentCore, and Strands
- AgentCore Memory highlighted as Demandbase's next area of exploration
Participants:
- Chad Holdorf – Vice President of Product Management, Demandbase
- Achint Naveen – Sr Account Manager, Amazon Web Services
See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Ep219: Scaling Autonomous Operations with AWS DevOps Agent and ServiceNow
ServiceNow and AWS reveal how the DevOps Agent and MCP Server Console are turning incident response into a fast, autonomous, fully governed process. Topics Include:Govind Menon (ServiceNow) and Arun Jacob (AWS) …
Shownotes
ServiceNow and AWS reveal how the DevOps Agent and MCP Server Console are turning incident response into a fast, autonomous, fully governed process.
Topics Include:
- Govind Menon (ServiceNow) and Arun Jacob (AWS) discuss MCP and A2A strategy.
- ServiceNow understands workflows; partners with AWS to power them with AI.
- AI Control Tower governs and secures agent access to enterprise data.
- MCP is the industry standard for how AI agents read and act.
- Action Fabric spans A2A, REST APIs, and MCP for agentic work.
- AWS DevOps Agent, built on Bedrock, resolves incidents through sub-agents.
- Admin and operator access patterns integrate with Dynatrace, Datadog, Slack, GitHub.
- Demo: ServiceNow incident automatically triggers DevOps Agent investigation and resolution.
- DevOps Agent writes findings live back into the ServiceNow incident ticket.
- ServiceNow champions capping MCP servers at 30 tools for performance.
- MCP Server Console lets teams build scoped, use-case-specific tool servers.
- NowAssist skills, Knowledge Graph, and REST APIs become MCP tools.
- Live demo connects a 38-tool custom MCP server to DevOps Agent.
- Role-based access ensures users only see their permitted MCP tools.
- ServiceNow's autonomous ITOM agents point toward unsupervised future operations.
Participants:
- Govind Menon – Head of MCP Product, ServiceNow
- Arunsingh Jeyasingh Jacob – Senior Solution Architect - ISV, Amazon Web Services
See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Ep218: Building Bizzdesign Unify - How Three Acquisitions became One AI Strategy
Chief Strategy Officer Nick Reed unpacks the "architecture of trust," AI-native enterprise transformation, and why staying laser-focused on customer value is central to Bizzdesign's bold AI strategy.Topics …
Shownotes
Chief Strategy Officer Nick Reed unpacks the "architecture of trust," AI-native enterprise transformation, and why staying laser-focused on customer value is central to Bizzdesign's bold AI strategy.
Topics Include:
- Bizzdesign: global enterprise transformation SaaS company with Dutch roots, founded 2000, Main Capital-backed
- Customers include HSBC, Shell, KPMG, and Airbus globally
- Bold 12-month strategy: acquired Mega International and Alfabet from Software AG
- Acquisitions tripled revenue, created the first true end-to-end enterprise transformation suite
- Bizzdesign’s 18-year recognition as a Gartner Magic Quadrant Leader in Enterprise Architecture
- The launch of Bizzdesign Unify in April 2026, an AI-native transformation collaboration platform
- Nick Reed's journey: enterprise software, customer value, M&A strategy, and AI-driven transformation
- How Bizzdesign supports planning, design, and governance pillars across the transformation lifecycle
- How Bizzdesign Unify complements existing enterprise architecture and portfolio management environments
- Why Bizzdesign Unify is architecturally different: conversational AI-native experience, not traditional UI
- AI acts as a co-worker, supporting transformation work and decisions through curated skills
- New experience opens enterprise context to broader stakeholders
- Bizzdesign Unify bridges the gap between messy whiteboards and governed enterprise data
- Example walkthrough: mapping customer service transformation dependencies and impacts
- Generative AI creates transformation scenarios grounded in enterprise context
- Tech stack built on Amazon Bedrock, MCP clients, graph data
- Balancing agentic AI and automation with human-in-the-loop accountability
- "Architecture of trust": permissions, oversight, and decentralized control
- Pricing shifts from seat-based to AI credit consumption model
- Closing advice: stay laser-focused on core customer value creation
Participants:
- Nick Reed – Chief Strategy Officer, Bizzdesign
- Kamil Davidov – Sales Leader Israel ISV-BizApps, Amazon Web Services
- Johan Broman – EMEA ISV Head of Solutions Architecture, Amazon Web Services
See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Ep217: Owning the Model: Conversational AI at Enterprise Scale with Omilia
Omilia’s CTO shares their strategy on building AI that facilitates billions of calls and what it takes to win the next decade.Topics Include:Miguel Alava welcomes Marios Fakiolas, CTO of OmiliaOmilia has built …
Shownotes
Omilia’s CTO shares their strategy on building AI that facilitates billions of calls and what it takes to win the next decade.
Topics Include:
- Miguel Alava welcomes Marios Fakiolas, CTO of Omilia
- Omilia has built production AI for over 20 years
- Banking and telco clients demand speed and accuracy
- Omilia builds its own agent framework and self-learning agents
- Infrastructure and data matter more than any single model
- Models are ships; Omilia's infrastructure is the permanent dock
- AI is core infrastructure at Omilia, not an external API
- Builders differ from orchestrators by owning bespoke models
- Platform is a kitchen; models are ingredients for recipes
- Omilia believes AI should be accessible, not just for elites
- AI vendors split into camps by economics and scalability
- Gen AI and ROI don't yet align well industry-wide
- Small unaddressed pain points can quietly sink AI projects
- Omilia revisits its offering using deep customer knowledge
- Cost-efficient economics at billions of calls is Omilia's moat
- Making AI work differs from making AI profitable
- Bedrock enables fast prototyping and early customer feedback
- Omilia moves to SageMaker AI to fully own its models
- Marios praises the AWS team supporting Omilia daily
- Speed round covers AI advocates, cloud, and adaptability ahead
Participants:
- Marios Fakiolas – Chief Technical Officer, Omilia
- Miguel Alava – EMEA ISV General Manager, Amazon Web Services
See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Ep216: Powering AI-enabled Operational Insights with Amazon Bedrock
From alert to root cause in one minute - how PagerDuty built autonomous incident response on Amazon Bedrock, and the future of triage and trust. Topics Include:PagerDuty's agents must perform during 2am outages …
Shownotes
From alert to root cause in one minute - how PagerDuty built autonomous incident response on Amazon Bedrock, and the future of triage and trust.
Topics Include:
- PagerDuty's agents must perform during 2am outages — stakes are high
- Software shipping accelerated dramatically; production environments largely did not
- A 9:30pm slowdown traced to a race condition solved two years earlier
- The fix was documented — but the context wasn't at hand
- PagerDuty Advance ships four agents: SRE, Scribe, Shift, Insights
- Why four, not one? Focus and predictability in non-deterministic systems
- Saurabh Shanbhag: Bedrock is far more than a model service
- Zero data retention, PrivateLink, TLS — why enterprises pick Bedrock
- Frontier models everywhere burns tokens; classify, route, distill, fine-tune
- SRE agent triages alerts before you even join the call
- One minute to root cause — context beat raw intelligence
- Human surfaces versus machine surfaces: MCP and CLI move fastest
- "The model eats the harness" — every upgrade invalidates foundational components
- Feeding agents everything failed; compartmentalised investigation threads work better
- New York Life's three stages of trust, and the seatbelt override that wasn't
Participants:
- Tom Hogarty - Senior Director Product Management, PagerDuty
- Saurabh Shanbhag – Sr Partner Solution Architect, Amazon Web Services
See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Ep215: Insight to Action: AI Agents Transforming Sales Operations
Domo and AWS reveal how AI agents freed sales reps from 20 hours of weekly busywork, turning scattered data into real-time coaching and forecasting.Topics Include:Domo and AWS teams introduce today's session on …
Shownotes
Domo and AWS reveal how AI agents freed sales reps from 20 hours of weekly busywork, turning scattered data into real-time coaching and forecasting.
Topics Include:
- Domo and AWS teams introduce today's session on AI agents in sales.
- Topic: using AI agents to transform sales operations, from insight to action.
- IT teams increasingly asked to turn data into actionable outcomes, not just access.
- Domo's CRO wanted AI agents to boost sales rep efficiency significantly.
- Reps act like "archaeologists," digging through scattered systems for basic context.
- This digging eats roughly 20 hours weekly, half of reps' time.
- Goal: personal AI agent per rep, understanding their book of business.
- Live demo begins: agent app surfaces urgent items needing attention.
- Agent tracks deal milestones, timelines, and forecasts from call and email data.
- "Deal coach" feature grades rep performance and suggests next actions.
- Agent tone can be tuned from gentle to direct, aiding tough feedback.
- Architecture overview begins: building an AI-ready data foundation first.
- Data from CRM, calls, and emails flows into a cloud warehouse.
- Two agents built: automated deal analysis and personalized deal coach.
- Agents write insights back to CRM, preserving human edit control.
- Recipe: build foundation, activate with agents, distribute to people.
- Governance must be embedded throughout, not bolted on afterward.
- Second example: Fogo do Chão uses AI to analyze restaurant reviews.
- AWS architecture explained: Domo runs on Bedrock, defaulting to Anthropic models.
- Q&A: sales team adoption was immediate and enthusiastic post-rollout.
Participants:
- Jason Longhurst – Head of Product Marketing, Domo
- Aman Tiwari - Sr Solutions Architect, ISV, Amazon Web Services
See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Ep214: Teradata, Amazon Bedrock AgentCore Unlock Zero-Data-Movement Analytics
Curious how AI can query your enterprise data without moving it or making things up? AWS and Teradata break down a trustworthy analyst agent built for real production use.Topics Include:Neha Wadhera (AWS) …
Shownotes
Curious how AI can query your enterprise data without moving it or making things up? AWS and Teradata break down a trustworthy analyst agent built for real production use.
Topics Include:
- Neha Wadhera (AWS) introduces Trinath Yarlagadda and the Teradata Analyst Agent
- Enterprise AI data prep is costly, stalling most orgs at experimentation
- Agent answers plain-English questions via traceable SQL, zero data movement
- Barrier removal drives 3.7x ROI and 40% productivity gains
- Healthcare demo setup: hospital COPD readmissions, ~$10K cost per incident
- Four design principles: traceability, no data movement, deterministic-first, governance as code
- Main orchestrator agent plans, writes SQL, calls Teradata MCP server
- Complex questions escalate to a context-isolated data scientist agent
- Built on Claude Agent SDK, running Bedrock Claude Sonnet/Haiku/Opus
- Live demo: COPD readmission rates explored through iterative agent reasoning
- Delegation demo: data scientist agent runs in-database analysis, surfaces factors
- Pre/post tool hooks log every step and cost to CloudWatch
- Agent hosted on Amazon Bedrock AgentCore, fully serverless and scalable
- AgentCore delivers runtime, memory, identity, and observability out of the box
- Lessons learned: guardrails first, deterministic ops, multi-agent registry, ongoing evaluation
Participants:
- Trinath Yarlagadda – Principal Solution Architect – Agentic AI, Teradata
- Neha Wadhera – Sr Solutions Architect, Amazon Web Services
See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Ep213: Prompt to Production - AWS Database Integration in Vercel
Learn how Vercel's "self-driving infrastructure" vision pairs with AWS databases to eliminate backend friction, securely cutting Aurora Serverless creation time from minutes to seconds.Topics Include:Hedieh …
Shownotes
Learn how Vercel's "self-driving infrastructure" vision pairs with AWS databases to eliminate backend friction, securely cutting Aurora Serverless creation time from minutes to seconds.
Topics Include:
- Hedieh Zandi (Vercel) and Manbeen Kohli (AWS) introduce prompt-to-production session
- Vercel powers 18 million developers, maintains Next.js and AI SDK
- Vercel's agentic infrastructure runs on AWS Lambda, CloudFront, and S3
- AI now generates frontend, APIs, and workflows for small teams
- Backend friction remains: credentials, provisioning, database configuration still hard
- Vercel envisions "self-driving infrastructure" that adapts automatically to apps
- New AWS partnership brings native Aurora DSQL and Postgres integration
- Manbeen explains databases now built into Vercel Marketplace and v0
- Aurora Serverless database creation sped up from minutes to seconds
- Aurora Postgres, DynamoDB, and DSQL scale prototypes without rewrites
- Pre-configured templates help builders start RAG or shopping AI apps
- Database security uses OIDC and IAM tokens, no stored passwords
- AWS chosen for agents: low latency, autonomy, one-click simplicity
- skills.sh gives agents reusable instructions, mirrors AWS Kiro's "powers"
- v0 lets users build full-stack apps using natural language prompts
- v0 uses Bedrock models and deploys directly on Vercel infrastructure
- Live demo: v0 builds restaurant app, provisions database, adds Stripe checkout
- Demo ends at AWS console; Rauch quote and hackathon close session
Participants:
- Hedieh Zandi - Product Lead, Vercel
- Manbeen Kohli - Director of Product Management, Aurora and RDS Databases, Amazon Web Services
See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Ep212: Reinventing with Agentic AI - How Kaltura Is Pivoting Their Platform for the Future
Kaltura's Ruthie Eisenberg and Yair Neumann reveal how the video giant is reinventing itself as an agentic digital experience company built on AI avatars and hyper-personalized content.Topics Include:Kaltura …
Shownotes
Kaltura's Ruthie Eisenberg and Yair Neumann reveal how the video giant is reinventing itself as an agentic digital experience company built on AI avatars and hyper-personalized content.
Topics Include:
- Kaltura founded 2006, went public on NASDAQ in 2021.
- Kaltura reinventing itself from video company to agentic digital experience company.
- Shift from static content delivery to hyper-personalized conversational experiences.
- Partners and customers now demand intelligence, not just video infrastructure.
- Kaltura's mission: powering agentic experiences across customer and learner journeys.
- AWS co-sell motion strengthened as Kaltura runs on AWS AI infrastructure.
- Camille used a Kaltura avatar to scale her own presentations.
- Most enterprise websites bury content behind thousands of static links.
- Kaltura builds personalised web pages on the fly, in real time.
- Over 80% of content users see is surfaced for the very first time.
- Acquisitions of eSelf.ai and PassFactory complete Kaltura's agentic content flywheel.
- PassFactory answers: what should this specific person see next?
- eSelf.ai enables multimodal conversational avatars that guide users emotionally.
- 20 years of behavioral data underpins Kaltura's content intelligence advantage.
- GPU scarcity and compute costs shape every AI architecture decision Kaltura makes.
- Kaltura optimises model tiers — strongest for planning, lighter models for execution.
- Fidelity, speed, and cost form a constant triangle in every AI product decision.
- Go-to-market and product teams now work closer together than ever before.
- Pricing shifting from seat-based SaaS to consumption and outcome-based models.
- Kaltura co-creating pricing frameworks with customers across different verticals.
- Internal product agent now handles research, stories, and data analysis autonomously.
- Small two-to-three person squads move fastest in the current AI environment.
- Yair's advice: fail at least once a week, succeed once a quarter.
- Kaltura scaled its CEO via avatar for a live investor earnings call.
- Ruthie's advice: keep the customer at the centre of every single decision.
Participants:
- Ruthie Eisenberg – Vice President, Strategic Partnerships, Kaltura
- Yair Neumann – Senior Vice President of Product, Kaltura
- Kamil Davidov – Sales Leader Israel ISV-BizApps, Amazon Web Services
- Johan Broman – EMEA ISV Head of Solutions Architecture, Amazon Web Services
See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Ep211: Going All In - How Monday.com Rebuilt Its Mission With Agentic AI
With 250,000 customers and $1.2B in revenue, Monday.com's CPTO explains why they threw out their roadmap and rebuilt everything around agentic AI.Topics Include:Daniel Lereya joined Monday.com when it had just …
Shownotes
With 250,000 customers and $1.2B in revenue, Monday.com's CPTO explains why they threw out their roadmap and rebuilt everything around agentic AI.
Topics Include:
- Daniel Lereya joined Monday.com when it had just 30 people and five engineers.
- He grew the R&D org from five engineers to roughly 900 over a decade.
- Three years ago Daniel became Monday.com's first ever CPTO.
- Monday.com initially approached AI by adding small features across the product.
- They called this early phase "sprinkling AI dust" — helpful but not transformative.
- A pivotal board meeting made Daniel realise AI hadn't changed Monday's core value.
- Monday.com decided to rethink its mission from first principles around AI.
- The new mission: AI agents that actually execute work, not just manage it.
- AI gives businesses an "infinite workforce" regardless of company size.
- Agents can now do hyper-personalised work at a scale humans simply cannot.
- Monday's platform puts agents at the centre, replacing boards and dashboards.
- Shared context and human-in-the-loop handoffs make their agents uniquely powerful.
- Monday ran an "AI month" — pausing the entire 900-person builder org to transform.
- The month rebuilt team mindset and energy, reminding staff of early startup days.
- Monday also ran an "agentic week" where every department built their own agents.
- Finance built agents to automatically match incoming payments to customer accounts.
- Scaling AI adoption internally remains the biggest challenge across businesses today.
- Monday introduced "effective AI" — balancing capability with cost efficiency.
- They acquired voice AI startup One AI to add specialised model capabilities.
- On pricing, Monday shifted to a hybrid seats-plus-AI-credits consumption model.
Participants:
- Daniel Lereya – Chief Product and Technology Officer, Monday.com
- Kamil Davidov – Sales Leader Israel ISV-BizApps, Amazon Web Services
- Johan Broman – EMEA ISV Head of Solutions Architecture, Amazon Web Services
See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Ep210: Resilience at Machine Speed - PagerDuty's Path to Autonomous Operations
PagerDuty SVP Rukmini Reddy explains why AI is making software operations exponentially more complex — and why the companies that learn and recover fastest will be the ones that win.Topics Include:PagerDuty …
Shownotes
PagerDuty SVP Rukmini Reddy explains why AI is making software operations exponentially more complex — and why the companies that learn and recover fastest will be the ones that win.
Topics Include:
- PagerDuty powers critical digital operations for enterprises and AI-native companies.
- Founded by early AWS employees who experienced always-on system failures firsthand.
- The platform evolved from simple alerting into a full operational intelligence platform.
- Complexity exploded with microservices, cloud-native infrastructure, and multi-cloud environments.
- Reliability must be a core value — not an operational afterthought.
- PagerDuty's culture champions the customer above everything else.
- Employee recognition extends beyond sales to celebrate the whole business.
- AI is accelerating software creation but making operations far more complex.
- AI fails differently — silently, unpredictably, with a much larger blast radius.
- Enterprises should leverage their operational history as a competitive AI asset.
- AI-native companies must build operational resilience early, not bolt it on later.
- The winners won't build fastest — they'll learn and recover fastest.
Participants:
- Rukmini Reddy – Senior Vice President of Engineering, PagerDuty
See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Ep209: Starburst Data's Blueprint for the AI Era with AWS
From cracked data foundations to multi-agent AI, Starburst Data's co-founder shares hard-won lessons on getting the right data, not just more of it.Topics Include:Matthew Fuller, co-founder and VP of Product at …
Shownotes
From cracked data foundations to multi-agent AI, Starburst Data's co-founder shares hard-won lessons on getting the right data, not just more of it.
Topics Include:
- Matthew Fuller, co-founder and VP of Product at Starburst Data, joins the show.
- Starburst is built on Trino, a fast SQL engine for federated data queries.
- Their platform lets users query data across lakes, stores, and databases seamlessly.
- Governed "data products" give organizations access to their full data estate in context.
- A strong data foundation is essential before any AI use case can succeed.
- AI doesn't create data problems — it exposes the cracks already there.
- Common mistake: assuming everyone in an org defines "customer" or "revenue" the same way.
- More data isn't always better — getting the right data is what matters.
- Customers include HSBC, Comcast, Zalando, ZoomInfo, and DBS, many running on AWS.
- AWS partnership spans technical support, SLA reliability, and proactive product briefings.
- Advice for product leaders: always anchor new technology back to the customer problem.
- 2026 will be defined by specialized multi-agents working together autonomously.
Participants:
- Matt Fuller – Co-Founder, Vice President of Product, Starburst Data
See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Ep208: Built to Survive: CockroachDB's Role in the Agentic AI Era
Find out why the world's largest banks and enterprises trust CockroachDB for mission-critical infrastructure, and what a decade of AWS partnership means for the future of cloud-native data.Topics …
Shownotes
Find out why the world's largest banks and enterprises trust CockroachDB for mission-critical infrastructure, and what a decade of AWS partnership means for the future of cloud-native data.
Topics Include:
- Cockroach Labs makes CockroachDB, a distributed SQL database built for resilience.
- It delivers cloud-native consistency that legacy relational databases simply cannot match.
- The name "cockroach" reflects survivability — it's designed to never go down.
- Target customers include major banks, trading platforms, retailers, and gaming companies.
- AI is forcing enterprises to accelerate database modernization from the board level down.
- AWS has been a foundational cloud partner for Cockroach Labs for a decade.
- The CockroachDB-AWS integration spans EC2, S3, Bedrock, and Amazon Q-Transform.
- AWS partnership shapes both product roadmap decisions and go-to-market execution.
- New partners should educate themselves first — AWS programs are deep and extensive.
- CockroachDB now supports native vector search for RAG and generative AI applications.
- Agentic AI could mean trillions of digital agents demanding real-time data infrastructure.
- Database modernization and AI adoption will only accelerate dramatically through 2027.
Participants:
- Cassie Zimmerman – Senior Director, Global Strategic Partnerships, Cockroach Labs
See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Ep207: The AI Arms Race: How Vectra AI Uses Agentic AI to Outpace Cyber Attackers
Greg Murphy of Vectra AI explains why no single security tool is enough in 2026, and how AI is transforming overwhelmed security teams into lean, highly responsive defense operations.Topics Include:Vectra AI …
Shownotes
Greg Murphy of Vectra AI explains why no single security tool is enough in 2026, and how AI is transforming overwhelmed security teams into lean, highly responsive defense operations.
Topics Include:
- Vectra AI helps enterprises detect and respond to cyberattacks before they become breaches.
- CISOs face millions of alerts monthly with dangerously understaffed security teams.
- Vectra pioneered AI-driven triage to prioritize only the most critical threats.
- The result: analysts act on two or three alerts, not thousands.
- Generative AI is now actively being weaponized by sophisticated bad actors.
- The first fully AI-orchestrated cyberattack by a nation state has already happened.
- Vectra and AWS Bedrock are building autonomous agents to fight back.
- Agentic AI can investigate thousands of incidents and surface only what matters.
- Over-reliance on single tools like EDR leaves dangerous gaps in defense.
- Modern attacks move fluidly across identity, network, and cloud environments simultaneously.
- AI stitches cross-surface signals together, revealing attacks hidden in isolated events.
- Best practice: assume breach, expand your network definition, and layer best-of-breed solutions.
Participants:
- Greg Murphy – Chief Business Officer, Vectra AI
See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Ep206: Building Agentic Products at Enterprise Scale with Datadog, Fireworks, Okta, Writer AI and AWS
If you're betting on agentic AI, hear directly from the builders navigating the challenges, innovating pricing models, and creating the coming reality where every employee manages thousands of agents.Topics …
Shownotes
If you're betting on agentic AI, hear directly from the builders navigating the challenges, innovating pricing models, and creating the coming reality where every employee manages thousands of agents.
Topics Include:
- Four panelists represent the full AI stack: build, run, secure, monitor.
- Agentic AI moved faster than anyone predicted just 18 months ago.
- Writer AI's no-code agent builder missed both its target personas entirely.
- Non-technical users now just prompt agents instead of building workflows.
- Fireworks AI processes over ten trillion tokens daily across open models.
- DeepSeek's Christmas release tripled Fireworks' capacity needs almost overnight.
- Okta identified agent identity as a security problem from day one.
- 91% of organizations are already using AI agents in some capacity.
- Datadog evolved naturally from dashboards to autonomous investigative agents.
- Bits.ai agents now diagnose production incidents before engineers wake up.
- Trust requires explainability — black-box agents stall enterprise adoption cold.
- Human-in-the-loop remains essential; risk tolerance varies wildly by organization.
- Writer AI compressed a four-month retail workflow down to one week.
- Multi-provider inference consistency is one of the hardest unsolved infrastructure problems.
- Agentic pricing models are fundamentally broken for enterprise budget planning today.
- Agents managing agents means every employee becomes a manager of thousands.
- POC data gaps are the most underrated blocker to production deployment.
- Security must be designed in from the start — retrofitting is painful.
- Build evaluations first so you know if you're actually improving anything.
- Find your uniquely differentiated data and build your agentic bet there.
Participants:
- Yannick Guillerm – Regional Manager, Sales Engineering, Datadog
- Ray Thai – Director of Product Management, Fireworks AI
- Andrew Yu – Vice President of Engineering, Okta
- Matan-Paul Shetrit – Director of Product Management, Writer AI
- Moderator: Carol Potts – General Manager, North America ISV Sales, AWS
See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Ep205: AI Teammates Are Here - Asana's Multiplayer Approach to an Agentic Future
Asana CPO Arnab Bose breaks down how AI agents are transforming collaborative work management with multiplayer AI teammates that any team member can coach and correct.Topics Include:Asana is a collaborative …
Shownotes
Asana CPO Arnab Bose breaks down how AI agents are transforming collaborative work management with multiplayer AI teammates that any team member can coach and correct.
Topics Include:
- Asana is a collaborative work management platform used by 170,000+ companies worldwide.
- The "Pyramid of Clarity" connects individual tasks all the way up to company strategy.
- Asana's "work graph" maps tasks, teams, projects, and portfolios in one connected system.
- Generative AI now converts unstructured data like emails into structured project plans.
- Asana integrates directly with AWS, Gemini, and Claude to automate that conversion.
- AI Teammates are first-party agents that take on and complete tasks inside Asana.
- These agents work in multiplayer mode — visible, collaborative, and team-correctable.
- A third AI unlock is coming: letting any external agent builder plug into Asana's interface.
- Asana runs entirely on AWS, including a new FedRAMP moderate GovCloud deployment.
- AWS Marketplace listings help customers transact faster using existing AWS credits.
- Arnab advises startups to bet on AWS long-term rather than chasing short-term LLM trends.
- His 2026 prediction: multi-agent orchestration standards will be the enterprise AI battleground.
Participants:
- Arnab Bose – Chief Product Officer, Asana
See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Ep204: Hacker Mindset at Scale: Inside Detectify's AI-Powered Security Platform on AWS
Discover how Detectify's hacker-DNA culture, multi-account AWS architecture, and Claude Code on Bedrock helped a lean security team deliver zero-day protection to customers before they even knew they needed …
Shownotes
Discover how Detectify's hacker-DNA culture, multi-account AWS architecture, and Claude Code on Bedrock helped a lean security team deliver zero-day protection to customers before they even knew they needed it.
Topics Include:
- Haris Kabiljagić leads cloud operations, data, and scanning at Detectify.
- Detectify started in 2013 as a group of ethical hackers in Stockholm.
- The platform continuously scans customers' attack surfaces for exploitable vulnerabilities.
- A global community of elite ethical hackers feeds real-world payloads into the engine.
- Early on, a centralized cloud team caused over 40 service disruptions monthly.
- A multi-account AWS strategy via Control Tower eliminated disruptions entirely.
- The hardest part of decentralizing wasn't technology — it was the culture shift.
- Cloud ops had to evolve from gatekeepers into enablers of secure, fast deployment.
- Detectify's architecture runs on three pillars: scalability, event-driven services, and security.
- MSK and Amazon MQ replaced self-hosted RabbitMQ, enabling true event-driven microservices.
- AWS Security Hub provides a single pane of glass for security posture visibility.
- When the CAPS zero-day hit, Detectify delivered a live test to customers same day.
- Customers logged in braced for panic — and found the threat was already handled.
- AI-assisted development via Claude Code on Bedrock accelerated code migration 30x.
- The team moved from five modules per week to 150, without sacrificing review quality.
- Success is now measured by speed of remediation, not volume of vulnerabilities found.
- A new internal scanner lets customers protect applications before they ever reach production.
- Detectify's roadmap: make security effortlessly native inside the pipelines teams already use.
Participants:
- Haris Kabiljagić - Head of Developer Services, Detectify
- Johan Broman – EMEA ISV, Head of Solutions Architecture, Amazon Web Services
See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Ep203: Beyond Observability - How Dynatrace Uses AI to Fix Problems Before You Know They Exist
Dynatrace's Chief Technology Strategist Alois Reitbauer explains how AI-powered observability is moving beyond monitoring to autonomously fixing software issues — and why the best AI doesn't replace human …
Shownotes
Dynatrace's Chief Technology Strategist Alois Reitbauer explains how AI-powered observability is moving beyond monitoring to autonomously fixing software issues — and why the best AI doesn't replace human judgment, it sharpens it.
Topics Include:
- Dynatrace helps global enterprise companies observe, optimize, and protect their software.
- The platform goes beyond monitoring — it takes automated action to fix issues.
- Business observability connects technical data to real-world operational decisions.
- Dynatrace has been investing in AI for 14 years, starting with root cause analysis.
- AI eliminates human confirmation bias when diagnosing critical system failures.
- Generative AI now enables Dynatrace to propose and implement code-level fixes.
- AI works best augmenting humans — like a GPS, not an autopilot.
- The Dynatrace-AWS partnership began with aligning on a shared long-term vision.
- Joint engineering calls and shared roadmaps made the two teams feel like one.
- Dynatrace experienced Amazon's famous silent document-reading meeting culture firsthand.
- Good partnerships require honesty, investment, and knowing when to say no.
- AI is maturing from an efficiency play toward genuine human augmentation.
Participants:
- Alois Reitbauer – Chief Technology Strategist, Dynatrace
See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
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