What is ai agent observability?
An agent run is a sequence of decisions. The model reads some context, decides to call a tool, reads the result, decides again, and repeats until it stops. Observability is the discipline of recording that sequence as it happens and making it inspectable afterward. Concretely, every run becomes an ordered stream of events - tool calls, model requests, hook fires, and errors - tied to a session, with timestamps, inputs, outputs, and token counts attached to each one.
The distinction that matters: a chatbot has one turn and one answer, so a transcript is enough. An agent has dozens of turns and a dozen side effects - files written, commands run, APIs hit - and the final message tells you almost nothing about how it got there. When an agent ships a broken migration or burns ninety model calls solving a two-call problem, the answer is never in the output. It's in the trace. Agent observability is what turns that trace from a wall of stdout into something you can actually reason about.
Put plainly: observability answers what did the agent do, in what order, and where did it go wrong - for a class of system that fails silently far more often than it crashes loudly.
Why logs aren't enough for agents
Every team reaches for the tools they already have. For agents that means application logs, an APM dashboard, and maybe an LLM tracing library bolted on. Each was designed for a world where a request comes in, deterministic code runs, and a response goes out. Agents break every assumption baked into that world.
- the failure is a decision, not an exception.A log line fires when something throws. But the expensive agent failures don't throw - the model picks the wrong file, edits it successfully, and every call returns 200. There is no error to log because, mechanically, nothing failed.
- latency is the wrong axis.APM tells you a span took 4 seconds. For an agent the question is almost never how long a call took - it's whether the call should have been made at all, and what the model believed when it made it.
- order and causality are the signal. Grepping logs gives you matching lines, scattered. An agent failure only makes sense as a path: this tool result led the model to that conclusion, which led to the bad edit. Flat logs flatten exactly the structure you need.
- the state is in the context window. Why an agent did something lives in what it had read by that point - the accumulated context. Logs capture outputs, not the evolving reasoning state that produced them.
You can approximate observability by stuffing more into your logs, but you end up rebuilding a trace viewer out of grep and timestamps. The shape of the data is wrong. Agents need a model built for runs of decisions, not lines of text.
What agent observability actually needs
Four capabilities turn raw events into answers. Drop any one and you have a partial picture that fails exactly when you need it most - during an incident.
1. a live event stream.Every run, ingested in realtime, newest-first, filterable by session and by event type. This is the heartbeat. When something looks off in production you watch the stream and see tool calls, model requests, and hook fires land as they happen - errors surfacing the moment they occur instead of after a batch job rolls up tomorrow's report.
2. session replay. Pick one run and step through it span by span on a trace rail. Replay is the difference between knowing a session failed and knowing why. You walk the run from the first prompt, watch the context accumulate, and find the exact span where the agent turned down the wrong path - reconstructing the agent's state at every step, not just its final answer.
3. queries over the data.Streams and replays handle one run; questions span thousands. “Which sessions called git push --forcethis week?” “What's the p95 model-call count per session by agent?” A query layer - SQL plus a visual builder over events, sessions, and evals - turns those into answers in seconds. Saved queries become the backbone of alerts and dashboards.
4. alerts and dashboards.You can't watch a stream all day. Alerting closes the loop: trigger on a metric threshold, a saved query, or an eval score, and open an incident the moment a failure mode crosses the line. Dashboards give you the standing view - error rates, llm-call efficiency, failure-mode breakdowns - so a regression shows up as a moving line, not a support ticket.
Together these are a funnel: the stream tells you something is wrong now, replay tells you why it's wrong on this run, queries tell you how often it's wrong across all runs, and alerts tell you the instant it starts being wrong again.
How agenteye does it
agenteyeis failproof ai's observability layer, and it implements all four pieces over one realtime ingest pipeline. The dashboard runs locally - by default at localhost:7771 - so your traces, prompts, and tool outputs never leave your machine.
realtime ingest.agenteye subscribes to the hooks your harness already fires and records every event as it happens. There's no SDK to wrap your agent in and no code to instrument - point it at the harness and runs start streaming. The live event stream, the trace rail for session replay, and the query layer all read the same store, so what you see in the stream is the same data you can replay span by span and aggregate in a dashboard.
the built-in agent.The capability that changes how people use it: agenteye ships an agent that turns a plain-English question into SQL, runs it, reads the matching trace, and names the failure mode for you. You ask “why did this session loop?” and it writes the query, walks the trace rail, and tells you the agent retried the same failing tool call eleven times - instead of making you write the SQL and eyeball the spans yourself. It collapses the four-piece funnel into a single question.
cli and mcp into local claude. agenteye ships a CLI and an MCP server so the data lives where you work. Run agenteye ingest --watch to stream runs in as they happen, or agenteye pull --since 7d to pull a window of history. The MCP server exposes that same trace data to your local Claude, so your coding agent can read what failed last time and fix it - every failure becomes context the next session learns from, instead of a lesson nobody captured.
Find, then fix
Observability is half the system. Seeing a failure mode is only valuable if you can stop it from recurring, and that's the second half of failproof ai: a find→fix loop. agenteye is the find half - it surfaces the failure mode out of the traces. The failproofai CLI is the fix half - the policy layer that prevents it from happening again.
The handoff is direct. agenteye shows you that, say, agents keep running terraform destroy on staging, or keep editing files outside the repo. You take that finding to the policy layer and write - or enable from the 39 built-in policies - a rule that denies or warns on that action at the harness hook layer. The next time the model tries it, the action is blocked before it lands, and agenteye records the block so you can confirm the policy fired. Find in observability, fix in policy, verify back in observability.
This is the difference between dashboards you stare at and a system that gets better. A failure mode you can see but not stop is just anxiety with a chart. The loop is what converts every observed failure into a permanent guardrail - the same way agent error recovery turns a detected failure into a mitigation, and the same posture that llm agent reliability depends on at scale.
Get started
agenteye and the failproofai CLI are free and open-source. Install the cli, run agenteye ingest --watch, and the observability dashboard comes up at localhost:7771 (the policy dashboard runs alongside at localhost:8020). Source lives at github.com/FailproofAI/failproofai and the docs at docs.befailproof.ai.