The Agentic Mesh Podcast

Secure Agent Series (4 of 4) #AgenticAI #AIAgents #CyberSecurity #EnterpriseAI #AIArchitecture  An agent can hold a perfectly correct authorization and still call the wrong tool, read the wrong data, or send output to the wrong place, unless something outside the agent checks every request before it executes. That check has three possible answers: yes, no, or hold. Unfortunately, most agent platfo...

#AgenticAI #AIAgents #CyberSecurity #EnterpriseAI #AIArchitecture  Agents now do useful work. Coding agents inspect repositories, write patches, run tests, and open pull requests. Business agents summarize accounts, triage cases, draft customer messages, prepare invoices, and move work across systems. That usefulness creates the control and governance problem. Once agents can touch repositories, t...

#AgenticAI #AIAgents #CyberSecurity #EnterpriseAI #AIArchitecture  Enterprise agents are starting to do real work before many enterprises can answer a basic question: which agent did it? Some agents were never registered. Some act through user credentials or shared service accounts (for example, coding agents like Claude Code or personal agents like OpenClaw). Others have no clear owner. When an i...

#AgenticAI #AIAgents #CyberSecurity #EnterpriseAI #AIArchitecture  Every CISO or security professional I speak with describes the same problem. "This new wave of agents operate with almost unbounded permissions," one told me. "I can't tell they are doing something wrong until after the fact," said another. A third put it most plainly: "These agents act like employees,...

#AICoding #AIAgents #SoftwareEngineering #DeveloperProductivity Agent memory is becoming a new foundation of enterprise agent architecture. But it seems like we are still treating memory as a bigger context window, a retrieval layer, or a better prompt. Instead, making your agents work well is turning out to be based upon how you manage its memory.  In fact, it looks like memory is turning into al...

Coding agents are already highly effective in the environment they were designed around: local context, local memory, local files, immediate feedback, and bounded technical surfaces. The tools are not a disappointment. They are a genuine shift in how contained technical work gets done. The gap between that and enterprise-scale production delivery is real and will not be closed by better model outp...

APA is the form factor for the enterprise agent ecosystem. Enterprises won't deploy one agent. They'll deploy millions — across functions, vendors, and models. The question isn't whether agents can reason or use tools. The question is what architecture makes them manageable, deployable, and governable at that scale. Agentic Process Automation is that architecture. Out Now!  O'Reill...

An agent harness is the runtime system that turns a model into an operating agent, but the harness required for a coding or personal agent is fundamentally different from the one required for an enterprise agent. Coding harnesses are built for workspace execution; enterprise harnesses are built for governed participation in long-running business processes under identity, policy, and trust. Out Now...

If Knowledge Management is the library, the Agentic Knowledge Fabric is the precision-curated briefing document prepared specifically for the agent’s current task. The Agentic Knowledge Fabric offers an architecture for token-aware knowledge serving. Out Now! O'Reilly Agentic Mesh written by Eric Broda and Davis Broda👉https://www.oreilly.com/library/view/agentic-mesh/9798341621633/👉Stay Connecte...

Agentic Process Automation puts agents inside enterprise workflows at scale — thousands of them, making decisions, routing work, invoking tools. Evaluating whether each one is safe to operate requires matching performance criteria to the governance risk each agent actually carries. That matching is the core problem. An agent can complete tasks quickly while violating policy. It can remain complian...

Agentic Process Automation has agents participating directly in business processes, making step-level decisions, interpreting mixed inputs, coordinating across systems, and operating within policy and control boundaries. That shift matters because many enterprise processes now depend on judgment over documents, messages, exceptions, thresholds, and domain rules that do not fit cleanly into determi...

What if agents could do more than assist developers — what if they became trusted participants in enterprise business processes? In this episode, we explore Agentic Process Automation (APA) and why it’s the next step beyond personal AI agents. While coding agents operate under individual credentials and user interfaces, enterprise agents must run headless, integrate across SaaS platforms, and meet...

We’re entering an era where enterprises won’t run a handful of agents, but thousands or millions of them. In this episode, we introduce Know Your Agent (KYA) and argue that managing agents must look more like managing employees than managing software. We break down why identity, access control, auditability, and trust are now urgent problems, how lessons from KYC, HR, and data governance apply to...

Most enterprise automation fails in the same place: the brittle gap between SaaS systems. In this episode of The Agentic Mesh Podcast, John Miller and Eric Broda argue that traditional RPA and workflows can’t handle ambiguity, edge cases, or real-world complexity. Instead, they explore agentic process automation, where AI agents powered by large language models fill the “white space” between platf...

Are coding agents enough, or do enterprises need a completely different class of AI to run their business processes? In this episode of The Agentic Mesh Podcast, we dive into the world of AI agents and the key differences between coding agents and enterprise agents. We explore how coding agents help developers write and iterate code, while enterprise agents tackle complex business processes, gover...

How well does your AI understand your business's decision-making processes? In our latest podcast, we highlight the critical role of policies in context engineering and how they can shape AI responses effectively. Don’t let your AI miss out on this vital information! The context window is one of the most critical and constrained resources in AI. In this episode of the Agentic Mesh Podcast, we...

In this conversation, Eric and John delve into the critical role of context in AI, particularly focusing on the concept of the 'minimum viable context' and its implications for AI agents. They discuss the challenges of managing context windows, the importance of decision traces and policies, and the need for trust in AI systems. The dialogue emphasizes the necessity of human oversight and...

In this conversation, Eric and John delve into the critical role of context in AI, particularly focusing on the concept of the 'minimum viable context' and its implications for AI agents. They discuss the challenges of managing context windows, the importance of decision traces and policies, and the need for trust in AI systems. The dialogue emphasizes the necessity of human oversight and the evol...

The hard problem isn’t building smarter agents; rather, if we are going to have hundreds, thousands, or maybe millions of agents in each enterprise, then it’s about building the social infrastructure for trust at-scale; it’s about making the entire agent ecosystem and the agents in them transparent, auditable, certifiable – and trusted. In this episode, John Miller and Eric Broda discuss the criti...

The future is ambient agents: always on, always listening, and always working. The hard part isn’t a prettier UI; it’s identity, state, and audit-ready agent-to-agent handoffs at scale—why A2A, MCP, and an Agentic Mesh exist. In this conversation, Eric and John explore the concept of ambient agents, which are AI agents that operate in the background, always on and listening. They discuss the impli...

Software is getting cheaper to build and insanely faster to ship, shrinking “ten people for a year” into days. However, the real question is this: if the tools are already here and the upside is that big, why are so many companies still stuck watching from the sidelines? In this conversation, Eric and John explore the emerging agent economy, discussing the rapid decline in software costs and deliv...

Today, far too many AI and agent projects die as science experiments. AI and agent projects live in a lab, impress at demo time, but they never get into production. In this conversation, Eric and John tackle this problem head-on. They discuss the challenges faced by AI projects, during the transition from proof of concept to production. They explore the socio-technical problems that hinder operati...

Agentic Mesh is the idea that the real challenge isn’t building a single impressive agent—it’s running an *ecosystem* of them in the real world. In this podcast, we treat agents like infrastructure: how do hundreds or thousands of autonomous agents find each other, coordinate work, and produce consistent outcomes without turning into chaos? Agentic Mesh is the enterprise-grade fabric that makes th...