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AI agents can shrink security work from days to minutes, but only if you can trust what you’re running. That’s why we sat down with Justin Buchanan, practice leader for Tenable’s Agentic AI Accelerator, to unpack a problem we don’t talk about enough: security teams are building powerful agentic AI workflows in private silos, even though cybersecurity has always thrived on sharing.

We dig into the Cyber Agents Exchange, an open source repository built for cybersecurity professionals to share AI agents, skill files, MCP servers, and playbooks across the tools you actually use. Justin walks us through concrete examples that hit daily SOC pain, like an agent that answers the brutally practical question “what do I need to fix today?” by combining vulnerability context with sources like MITRE and CISA KEV. We also cover hunt automation that ties together SIEM and EDR data so analysts spend less time reconciling timestamps and more time making decisions.

Then we get serious about AI security risk and governance. Justin explains how contributions are baseline reviewed to catch credential exposure, undocumented outbound calls, and unsafe behavior, plus what it takes to earn a higher-confidence “vetted” status with deeper inspection and sandbox testing. We also talk about Swarm community build events, why more non-traditional builders are shipping real cybersecurity automation, and what CISOs need to scale agentic AI safely inside the enterprise.

If you’re experimenting with AI agents for cybersecurity, threat hunting automation, SOC efficiency, or secure open source adoption, this conversation gives you a practical framework. Subscribe, share this with your team, and leave a review with the AI agent you most want to build next.

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AI didn’t make application security “new” so much as it made it faster. When exploits can be drafted, refined, and scaled with AI assistance, the window to detect and fix vulnerabilities gets brutally short. That urgency is why we sat down with Grant Ongers — OWASP Foundation, a longtime application security leader and OWASP project lead, to talk about what’s changing, what isn’t, and what teams should do right now.

We start with how OWASP works in the real world: open source security projects, global AppSec conferences, and local chapters that help grow the next generation of AppSec talent. From there, we dig into the two big AI shifts Grant sees everywhere: accelerated exploitation and a lowered barrier to entry for attackers. If the “bad guys” can move faster, we need to move faster too, and that means pairing solid fundamentals with smarter, AI-assisted defense.

Threat modeling is the practical backbone of that defense. Grant breaks it down into an approachable four-question framework and explains how AI can help teams explore “what can go wrong” without outsourcing responsibility. We also talk about OWASP Cornucopia, a card-based, gamified approach that helps developers surface security abuse cases and connect them to concrete guidance like OWASP ASVS. Finally, we get blunt about agentic AI: treat agents like employees, scope access tightly, watch for toxic combinations of permissions, and keep a human accountable for anything that ships.

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AI is everywhere right now, but most companies are still stuck in the same place: promising pilots that never turn into real production results. We sit down with Kathryn Harrison, Global Vice President of Strategy and AI Platforms at Concentrix to get practical about what actually scales in enterprise AI, especially when you are responsible for operations and customer experience at global scale. If you are trying to prove AI ROI, reduce cost, grow revenue, or improve customer satisfaction without creating new risk, this conversation is built for you.

We walk through the real “AI gap” between executive expectations and day-to-day delivery, plus a simple maturity model that shows where most organizations sit today: individual copilots, narrow functional pilots, and full workflow reinvention with agentic AI. From there, we dig into orchestration, the make-or-break work of deciding what AI should handle, where humans must stay in the loop, and how to make handoffs seamless so customers do not have to repeat themselves. Catherine also explains how to encourage fast experimentation while keeping security, compliance, and governance solid through role-based access, clear guardrails, and cross-functional oversight.

Then we get into what teams often ignore until the bill arrives: token shock. We talk tokenomics, why bolting AI onto an old process can cost more than the original workflow, and how CFOs and CIOs should treat AI spend as a capital allocation decision. We wrap with what is coming next, including agentic commerce and a glimpse at what a truly agent-driven Black Friday could look like across retail, travel, banking, and healthcare.

If this helped you think more clearly about enterprise AI strategy, agentic workflows, and customer experience automation, subscribe, share with a builder or operator, and leave a review with your biggest takeaway.

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Your AI agent can write SQL in seconds. The hard part is letting it touch real data without turning production into a crime scene.

We sit down with Mike Freedman, co-founder and CTO of Tiger Data (formerly TimescaleDB) and a Princeton computer science professor, to unpack what “real-time data infrastructure” looks like now that AI agents are becoming everyday teammates. We talk about why the Timescale name started to box the company into a narrow “time series database” perception, even though many teams use Postgres as a unified operational database that combines telemetry, events, and business metadata in one place.

From there, we dig into why Postgres is having a second wave with AI: a stable, trusted core plus an extension framework that lets the ecosystem innovate with time series, PostGIS, and vector search. Mike explains why industrial data is growing faster than legacy proprietary “box in a closet” systems can handle, and why modern industrial historians separate compute and protocol layers from scalable storage so factories, power grids, and monitoring dashboards can evolve like modern software.

Then we get concrete on agentic Postgres. Agents move fast, operate in parallel, and are surprisingly strong at SQL, which raises new needs: hybrid search inside the database, safe tool calling (including MCP-style interfaces), and rapid database branching so an agent can explore a fresh snapshot of a multi-terabyte database in seconds, test ideas, and spin it down at near-zero cost. We close with what’s next for Tiger Data across cloud and edge deployments, including the rising importance of AI data centers as industrial-scale control systems, plus Mike’s advice for students building careers in a rapidly shifting AI era.

If you got value from this, subscribe, share it with a builder on your team, and leave a review with one question you want agents to answer from your data.

Learn more about Microsoft's ISOC announcement: https://fandf.co/4iQ9b5n

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Enterprise AI is everywhere, yet most teams still struggle to move beyond pilots. We sit down with Jason Olkowski, Chief Strategy and Transformation Officer at Creatio to unpack what the data actually says about who is succeeding with enterprise AI adoption and why. One surprising insight: the job-cut narrative is overplayed. The strongest programs use AI to redeploy capacity, automate the right work, and help people do more high-value thinking, selling, and serving.

We dig into what separates the top adoption leaders from everyone else, including how they embed AI across the business, automate a larger share of critical workflows, and scale AI agents across departments instead of leaving AI as a personal assistant. Jason explains why governance is not a compliance checkbox, but a practical system for building, deploying, orchestrating, monitoring, and auditing agentic automation safely in production.

You’ll also hear concrete examples of AI agents inside CRM, like morning account briefings, voice-based updates on the go, and automated post-meeting follow-ups that push clean intelligence back into the system of record. We wrap with pragmatic advice for CIOs under board pressure: pick a governed, LLM-agnostic platform, start with repeatable workflows for fast wins, then reinvest the freed capacity into tougher friction points that slow the business down.

If you’re trying to turn enterprise AI strategy into measurable impact, subscribe, share this with a colleague who owns AI governance, and leave a review with your biggest “where do we start?” question.

Learn more about Microsoft's ISOC announcement: https://fandf.co/4iQ9b5n

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AI gets a lot messier the moment it leaves the chat window and touches real sensors, machines, and buildings. We sit down with Anthony Canterbury, Head of Engineering and Architecture, JIG-SAW US to dig into AIoT, the intersection of AI and industrial IoT, and why reliability and safety matter more than hype when you are dealing with edge computing, automation, and real operational risk.

We talk through what has actually changed over the last couple decades of machine intelligence, from expert systems and early neural nets to today’s transformer models. Then we get practical: device onboarding, decoding weird formats, parsing, and data normalization. Anthony explains why pairing LLMs with deterministic algorithms can remove a huge amount of manual integration work, and why in-house benchmarking beats trusting public leaderboards when your data is specialized and messy.

From there we tackle the uncomfortable part: hallucinations. If you simply bolt a chatbot onto an existing platform, you can see extreme error rates on IoT workflows. The path forward is architectural, not cosmetic: constrain the model, validate outputs, specialize where needed, and keep a human in the loop so alerting systems evolve without becoming a false-alarm factory. We also dig into treating cloud APIs like “devices,” plus when small, custom edge models win on security, cost, and accuracy for sensitive environments.

If you care about industrial AI, IoT security, edge AI, and building systems that work in the real world, this one is for you. Subscribe for more, share this with a CTO or engineer who is building with AI, and leave a review with your biggest question about AIoT reliability.

Learn more about Microsoft's ISOC announcement: https://fandf.co/4iQ9b5n

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Only 35% of executives say AI consistently delivers the business outcomes they expect, and that single stat explains why “AI everywhere” suddenly feels risky. We sit down with Sushant Tripathi Vice President & North America Lead – AI & Digital Transformation from TCS to talk about what changes when enterprises move beyond experimentation and put autonomous agents into real business operations, where the cost of a bad decision is measured in customer harm, regulatory exposure, and millions in downtime. 

We dig into why classic IT reliability metrics like uptime and latency are no longer enough. AI reliability now includes service-level experience, trust, and the ability to prove what happened and why it happened. Sushant breaks down how enterprises should think about trustworthiness as an engineering responsibility, not just a compliance checkbox, and why “more model capacity” does not automatically create dependable outcomes without strong data, integrations, evaluation, and observability. 

We also get practical about controls: why many companies can detect AI failures but far fewer can stop them, what “kill switch” thinking looks like, and how an AI maturity journey changes the right level of autonomy. Finally, we talk governance and ROI: who inside the organization must own the policies that let agents act, and how to turn individual productivity gains into measurable enterprise KPIs like revenue, faster resolution, and better customer experience. 

If you’re building enterprise AI, agentic AI, or AI governance programs, this is the reliability playbook you’ll want. Subscribe, share this with a leader who owns risk or operations, and leave a review with your take: what is the first guardrail every autonomous agent should have?

Learn more about Microsoft's ISOC announcement: https://fandf.co/4iQ9b5n

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AI agents are starting to run real customer workflows, and the hardest part isn’t getting a demo to work, it’s knowing the agent is behaving correctly at scale. We talk with Mukul Karnik Vice President of AWS Observability at Amazon Web Services about what “observability for AI agents” actually means when the system can be up, fast, and still wrong. Think guardrails, accuracy, and policy compliance alongside the classic metrics like latency and availability.

We get specific about failure modes that don’t look like normal software bugs: an agent that confidently approves a hazmat return because a user asked the question in an odd way, or an agent that answers something it should refuse. Mukul explains how evals act like tests for agent behavior, how tracing and telemetry help you see tool calls and dependencies end to end, and why a unified view matters when the agent relies on apps, databases, and infrastructure to get the job done. We also dig into what CloudWatch Omni is aiming for with an AI-first experience, including deeper investigations that can be driven by a DevOps agent for root cause analysis.

We also cover how teams can start earlier in the workflow with a free local IDE plugin to visualize agent traces, then bring the same evals and monitoring into production. On top of that, we talk multi-cloud realities, supporting different agent frameworks, and why the line between observability and security keeps getting blurrier as more signals live in the same logs and telemetry. If you’re building agentic systems, this is a practical look at monitoring, debugging, and building trust in autonomous behavior.

Subscribe for more conversations on AI infrastructure and production engineering, share this with a builder on your team, and leave a review with the toughest agent reliability problem you’re trying to solve.

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Robots are leaving the lab, and the real world does not hold still. We sit down with Dr. Amarjot Singh Founder and CEO of Skylark Labs to unpack what “physical AI” actually means when a machine has to operate outdoors, in hazardous environments, and keep learning after deployment, without a safety net of perfect data or constant cloud access.

We walk through mission-critical examples where edge AI and continual learning are not optional. Think military tarmacs where a 1 cm piece of debris can cripple a jet engine, and where new debris types and surface imperfections can trigger costly false positives. We also get into why on-device learning changes the game: lower latency, better privacy, and resilience when connectivity drops. Amarjot shares how Skylark shrinks models onto edge hardware so robots, robotic dogs, and other platforms can adapt in the field instead of waiting for a retraining cycle back at HQ.

Then we go deep on drones and UAV autonomy. The conversation moves beyond “spot the crack” into drones that can take action with robotic arms, from cleaning solar panels in dusty deserts to applying quick fixes on industrial equipment. That upgrade introduces hard control problems: once a drone touches the world, it has to handle new forces, wind, and unexpected obstacles like scaffolding, all while staying stable and safe. We also cover what still holds the industry back, including data bottlenecks and rugged humanoid hardware needs like waterproofing, safer batteries, and stronger hands.

If you care about robotics, edge computing, autonomous drones, and mission-critical humanoid robots, subscribe, share this with a friend, and leave a review so more people can find the show.

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US tech companies don’t usually lose in Europe because the product is bad. They lose because they market like Europe is one big country. From Berlin, B2B marketer and podcast host William Delaney joins us to compare how SaaS and tech companies sell, market, and grow across Europe versus the United States, especially in traditional industries like manufacturing, pharma, and logistics where trust is everything and “move fast” is not the default setting.

We dig into a simple but revealing contrast: in many European markets, buyers want the “instruction manual” before they buy. That shows up as longer decision cycles, more cautious adoption of new tools, and a stronger demand for proof. William shares why testimonials and human credibility often beat ads, why feature-driven product marketing can matter more than broad “solutions” language, and how community-led growth can open doors when you’re an unknown vendor.

We also get practical about localization, not just translation. Messaging that works in the UK and Ireland can fall flat in Germany, Austria, or Benelux, and the tone shifts again in Mediterranean markets. Then we tackle GDPR and data privacy: when regulation slows scaling, when it strengthens long-term trust, and how that intersects with AI adoption right now. If you’re building a go-to-market strategy for Europe or trying to sell complex B2B software anywhere, you’ll leave with clearer angles, better expectations, and tactics you can apply immediately. Subscribe, share this with a founder or marketer who needs it, and leave a review with your biggest Europe go-to-market question.

Learn more about Microsoft's ISOC announcement: https://fandf.co/4iQ9b5n

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