How do I track what my AI agent is doing in real time?
You track an agent in real time by watching records land as each call runs, not by waiting for its recap afterward. Route the work through Zambo, the verifiable work layer for AI, and open the live receipt stream to see each call and its saved result as it files. Every receipt follows AER-1, IETF Internet-Draft draft-zambo-aer1-26, and a receipt proves the saved result was not changed, not that the result was right. Watch it happen on the live receipt stream.
What is AI agent observability?
AI agent observability is the practice of making an agent's work visible while and after it runs: which tools it called, in what order, with what inputs, and what came back. On Zambo it goes one step past dashboards, because each call through its 153 tools, 35 on the default surface, files a verifiable AER-1 receipt that anyone can recheck later. HANDOVER, the flagship, carries that record from one AI to the next so the history survives a switch. Check a record yourself at zambo.dev/verify.
What AI agent observability actually is
Observability answers three questions about an agent's work: what ran, what did it produce, and can someone else check that record later? If the only answer is the agent's own chat message saying the task is done, you do not have observability. You have narration.
There are two kinds of visibility, and they serve different people. Internal telemetry serves the team running the agent: traces, latency, token counts, error rates. Evidence grade logging serves anyone who has to trust, pay for, or audit the work: a per call record, filed at execution time, that a second person can open and recheck without your login and without trusting your system. Zambo offers the second kind, for calls made through its tools.
The three layers of watching an agent on Zambo
1. Watch calls land live
The public receipt stream shows Zambo calls as they land, in public. If your agent is working through Zambo right now, its calls appear there as receipts. That is the real time view this page can honestly point to: a live stream of per call records, not a private dashboard.
2. Open the record for any single call
Every call gets its own page at zambo.dev/run/ plus the receipt id. It names the tool, the timestamp, the recorded input, the status, and the saved result, with a digest over that result. Share the link with a client, a manager, or a teammate and they see the same record you see.
3. Carry the history across agents
Work rarely stays in one session. When a job passes to another agent or resumes later, a handover carries the record with it, so the history of the job stays in one readable chain instead of scattering across chats that scroll away.
How do I log every action my AI agent takes?
Log at the point of execution. Connect the agent to the MCP endpoint at https://zambo.dev/mcp and run the actions that matter through Zambo tools. Each call is recorded as it executes and filed as a verifiable receipt, per AER-1 (draft-zambo-aer1-26), the open specification for the receipt format.
- Connect your AI or agent to https://zambo.dev/mcp. No account and no API key for the first call.
- Run the work. Every tool call returns its receipt and public run link in the response.
- Keep the run links. Together they are the log: ordered, per call, and openable by anyone you share them with.
- Check any entry later at zambo.dev/verify/. A match confirms the saved result was not changed since it was saved.
For the difference between a plain log and a receipt, read AI agent log vs verifiable receipt. For the day view of the same records, read the AI agent activity log.
How do I see a history of everything my AI agent did today?
Start from the records, not the recap. Open the receipt stream for today's calls, then open each receipt in time order. Each one shows the tool, the time, the input, and the saved result, so the day reads as a sequence of checkable entries. If the work crossed sessions or agents, follow the handover chain and the full day stays connected.
One honest boundary: the history covers calls made through Zambo tools. A call your agent made on another platform leaves no Zambo receipt and will not appear, which is exactly why the routing decision matters before the work starts, not after someone asks for the history.
Best tools to monitor AI agent activity: choose by the job
There is no single ranking that fits every team, because monitoring splits into two different jobs.
Job one: internal telemetry for the team running the agent
You want traces, latency graphs, error alerts, and cost detail inside your own stack. That is tracing and APM work. Zambo does not do it, and this page will not pretend otherwise: there are no alerts and no latency graphs here. Read AI agent receipts vs tracing and AI agent monitoring vs verification for how the two approaches differ.
Job two: evidence you can show someone
You want a record a client, a manager, or an auditor can open without your login and recheck without trusting you. That is evidence grade logging: a verifiable receipt per call, a live public stream, and run links that stay readable later. That is the job Zambo does, for calls made through its tools. Many teams run both kinds side by side, one to operate the system and one to prove what it did.
How do companies audit autonomous AI agents?
An audit stands on records that survive the audit. The pattern that works: keep a per call record for the agent's work, hand the auditor the links, and let the auditor check each record independently, with no account and no help from the team being audited. On Zambo, each receipt opens at its public run link and checks at zambo.dev/verify/, and handovers keep multi agent work in one chain.
Receipts prove the saved result was not changed since it was saved. They never prove the work was right. A good audit uses receipts to fix the record in place, then judges the content of that record on its merits. For the deeper reads, go to the AI agent audit trail and the agentic workflow audit, which cover rebuilding and reviewing a finished run from its records.
Honest limits: what this observability does not cover
- Zambo calls only. Receipts cover calls made through Zambo's tools. Calls your agent makes on other platforms leave no Zambo record.
- No internal reasoning. A receipt records a tool call and its saved result. It does not record what the agent was thinking between calls.
- Not a tracing or APM dashboard. There are no alerts, no latency graphs, and no live performance monitoring here. The real time element is the public receipt stream and the per call records, exactly as verified on this page.
- A receipt is not a verdict. It proves the saved result was not changed since it was saved. It never proves the answer was right, complete, or wise. That judgment stays with the person reading the record.
Keep the record running
Single jobs need no pass at all: run the first call in Zambo Chat or through the MCP endpoint and keep the receipt. When observable work becomes your daily routine, the paid rungs are simple. The $0.99 Day Pass for 24 hours covers a day of agent work. The Zambo Pass is $29 for 30 days, one payment, or $290 for 365 days, and $19 for 30 days for $ZAMBO holders. Every rung keeps the same rule: a verifiable receipt for every call, at a link anyone can check.
Questions, answered
How do I track what my AI agent is doing in real time?
Route the work through a system that records each call as it happens, then watch the records land. On Zambo, every tool call made through https://zambo.dev/mcp leaves a verifiable receipt at a public link, zambo.dev/run/ plus the receipt id, and the public receipt stream shows calls as they land. Open the stream to watch activity live, open any receipt to see the tool that ran, the time, the input, and the saved result, and use the verifier at zambo.dev/verify/ to check that the saved result was not changed since it was saved. That covers calls made through Zambo tools. It does not show an agent's internal reasoning or calls made on other platforms.
What is AI agent observability?
AI agent observability is the practice of making an agent's work visible after and while it happens: which tools it called, in what order, with what inputs, and what came back. Good observability answers three questions without asking the agent to narrate itself: what ran, what did it produce, and can someone else check that record later. There are two kinds. Internal telemetry serves the team running the agent, with traces, latency, and error detail. Evidence grade logging serves anyone who needs to trust or audit the work, with a per call record that can be opened, shared, and rechecked. Zambo offers the second kind for calls made through its tools.
How do I log every action my AI agent takes?
Log at the point of execution, not from the agent's own summary. Connect your agent to the Zambo MCP endpoint at https://zambo.dev/mcp and run its tool calls there. Each call is recorded as it executes and filed as a verifiable receipt with the tool name, the timestamp, the recorded input, and the saved result, at a public run link you can keep. Calls your agent makes anywhere else are not recorded by Zambo, and its internal reasoning is not a tool call, so route the actions that matter through the endpoint and treat the receipt list as the log of record.
How do I see a history of everything my AI agent did today?
Open the public receipt stream and work backwards through today's receipts, or keep the run link from each call and open them in order. Each receipt shows the tool, the time it ran, the input, and the saved result, so a day of work reads as a sequence of records rather than one end of day recap. When work passes from one agent or one session to another, a handover carries the record with it, so the history survives the switch. The history covers calls made through Zambo tools; calls made elsewhere leave no receipt and will not appear.
How do companies audit autonomous AI agents?
An audit works from records, not recaps. The company keeps a per call record for the agent's work, an auditor opens each record independently, and checks that the saved result matches what was filed at execution time. With Zambo, the auditor needs no account: each verifiable receipt opens at its public run link, and the verifier at zambo.dev/verify/ confirms the saved result was not changed since it was saved. Handovers carry the record between agents and sessions so the chain stays readable end to end. A receipt proves the saved record was not changed; it does not prove the work itself was right, so auditors still judge the content. The deeper reads on this hub are the AI agent audit trail page and the agentic workflow audit page, linked below.
Best tools to monitor AI agent activity?
Choose by the job, not by a ranking. If the job is internal telemetry, meaning traces, latency, error rates, and alerts for the team running the agent, you need a tracing or monitoring tool built for that, and Zambo is not one: it has no alerts and no latency graphs. If the job is evidence you can show someone, meaning a per call record a client, a manager, or an auditor can open and recheck without your login, that is evidence grade logging, and it is what Zambo does for calls made through its tools: a verifiable receipt per call plus a live public stream. Many teams need both kinds, one to run the system and one to prove what it did.
Keep reading: log every action my AI agent takes and proof of work for AI generated pull requests.