When an agent fails at step 47 of a 50-step workflow, maybe verifying a hotel booking across a regional site that just changed its confirmation page, you don't get a stack trace. You get silence, or worse: a "success" message with subtly corrupted data that won't surface until a customer complains three days later.

Alex Reibman hit this wall last summer at a San Francisco hackathon. He built AI agents to scrape the web.

No logs that made sense. No way to replay what happened. No visibility into which decision went wrong or why the agent thought it succeeded when it clearly hadn't.

That frustration led him to start AgentOps (now Agency AI) with co-founders Adam Silverman and Shawn Qiu. The problem they're tackling: the tools we have for understanding software weren't designed for systems that make probabilistic decisions across hundreds of coordinated steps.

Traditional debugging assumes you can reproduce the error, trace the execution path, identify the faulty line. Agents don't work that way. They make decisions based on context that shifts, call tools that might return different results, coordinate with other agents whose state you can't see. The "code" isn't just code. It's a dialogue involving external tool calls, memory lookups, and reasoning steps that exist only as token probabilities. Root causes hide several steps upstream from where failures become visible.

Reibman recognized that agents need different observability infrastructure. AgentOps tracks what traditional monitoring can't capture: LLM calls, costs, latency, agent failures, multi-agent interactions, tool usage. All timestamped and traceable. More importantly, it provides the ability to rewind and replay agent runs with point-in-time precision, jumping to failure points and re-running from checkpoints without executing the full flow again.

Co-founder Adam Silverman frames the platform as:

Just as IT teams need to know which employees accessed what systems when, agent infrastructure needs similar visibility. Not for surveillance, but for accountability and debugging. When an agent makes a purchasing decision or extracts competitive data, you need to reconstruct exactly what it "saw" and why it acted.

When we're orchestrating thousands of browser sessions across fragmented web surfaces at TinyFish, we've hit this exact wall. An agent successfully navigates 47 authentication steps, handling cookies, session tokens, regional redirects, then suddenly fails on step 48 because a site's CAPTCHA behavior changed in one geography. Traditional logs show "request failed." That tells us nothing about which reasoning step went wrong, what the agent actually saw on that page, or why it thought its approach would work.

You can't optimize what you can't see. You can't debug what you can't replay. You can't deploy with confidence what you can't monitor.

Reibman's work shows how young this field actually is. We're still building the basic infrastructure that software engineering took for granted decades ago: debuggers, profilers, monitoring tools. The difference is that agent systems require different approaches because they're not deterministic. We're in the infrastructure-building phase of agent development, not the application-building phase.

By making agent behavior visible and debuggable, infrastructure like AgentOps transforms agent development from exploratory process into engineering discipline. It means you can run regression tests when you update prompts. It means you can identify which 3% of edge cases cause 80% of failures. It means you can deploy agents knowing that when something breaks, and it will break, you'll understand why within minutes, not days.

For systems that need to work reliably at scale, this is table stakes. The step 47 problem isn't going away. But infrastructure that helps you understand and fix it separates demos from production.