Eric M. Broda

@ericmbroda Solid framing, the seven dimension model aligns with practical governance needs

Ambient Agents are Full Participants in Business Processes — They are not Chatbots, Co-Pilots, or… medium.com/p/ambient-agen…

Agent Evaluation Framework: The Hard Part is Judging Agents medium.com/p/agent-evalua…

Agent Evaluation Framework: How do Enterprises Know Which Agents are Ready for Real Work? medium.com/p/agent-evalua…

Ambient Agents — Always On, Always On, Always Working medium.com/p/ambient-agen…

Agentic Knowledge Fabric: Turning Enterprise Content into Agent Context medium.com/p/agentic-know…

Agentic Knowledge Fabric: Semantics for Agent Context Generation medium.com/p/agentic-know…

Agentic Knowledge Fabric: Addressing The Most Pressing Agent Knowledge Challenges medium.com/p/agentic-know…

Introducing Agentic Knowledge Fabric (Agentic Process Automation, Part 3) medium.com/p/introducing-…

What's the biggest challenge you've encountered while scaling agents in production? This Friday, @ericmbroda (Eric Broda) joins the @AI_TCG_CNCF (AI Technical Community Group) to discuss using Kubernetes as the control plane for agent fleets. Details: community.cncf.io/events/details… #Kubernetes #AgenticAI #CNCF #CloudNative

Agentic Knowledge Fabric: The Foundation for Agentic Process Automation medium.com/p/agentic-know…

I wish I had articulated it this cleanly. But this is absolutely on point, and will get only more visible and obvious in the coming months.

Agentic Process Agents Implement Business Processes medium.com/p/agentic-proc…

We Need Enterprise Process Agents — Unfortunately Most Agents are Simply Personal Productivity… medium.com/p/we-need-ente…

Agentic Process Automation (Part 2): From Personal Agents to Enterprise Process Agents medium.com/p/agentic-proc…

An Agent Trust Framework medium.com/p/an-agent-tru…

I tried @NotebookLM's new Cinematic Video Overview with my latest deep dive: "Your New Job Is to Onboard AI Agents" Honestly, it came out super well. Really impressed by how it created its own structure and narrative. 📌 Watch below:

I've been using Claude Code heavily lately, and while doing so, I've been casually watching the OpenClaw codebase evolve. What I've witnessed mirrors a pattern I've seen play out with every agent framework before it — and it's worth talking about. OpenClaw is a remarkable project. It went from zero to one of the most-starred repos on GitHub in under a week. And now, with AI agents actively contributing to its own development, the codebase is doing something extraordinary: it's expanding at a pace no human team could match — or meaningfully oversee. A month ago, the repo sat around 400k lines of code. Now it's pushing 1 million. Daily commits are holding steady above 500. There's even a lean fork — nanobot — that replicates the core functionality in roughly 4,000 lines, advertising itself as "99% smaller." That contrast alone tells you something important about what's happening to the original. From a software engineering standpoint, this is not a sign of health. Velocity without comprehensibility is just entropy with good PR. What we're witnessing is a codebase that has crossed a threshold: it is no longer humanly maintainable. No engineer can meaningfully review these commits. No architect can hold the system model in their head. Technical debt isn't accumulating — it's compounding, at AI speed, every single day. This raises a question I can't stop thinking about: Does there exist any project in the world that can grow sustainably — maintaining architectural clarity while continuously expanding functionality — with zero meaningful human involvement? Not "AI assists humans," but genuine autonomous stewardship of a living codebase? If that's possible, then what kinds of projects still can't be fully AI-maintained today? Is it complexity? Ambiguity in requirements? The need for taste and restraint? And the deepest question: will we eventually reach a point where every software project can be fully maintained by AI — including the AI systems doing the maintaining? My instinct is this: AI is extraordinarily good at local optimization. Write this function. Fix this bug. Add this feature. But "keeping a system simple" is not a local problem. It requires global aesthetic judgment — the ability to say "we could add this, but we shouldn't." That kind of restraint might be the last genuinely human contribution to software engineering. Or maybe I'm wrong. Maybe future AI systems will develop something like taste. Maybe they'll learn that the most important code is often the code you don't write. I genuinely don't know. But watching a codebase grow from 400k to 1M lines in a single month, driven almost entirely by agents, makes me feel like we're all about to find out — whether we're ready or not.

Trust — The Price of Admission for the Agent Ecosystem medium.com/p/trust-the-pr…