How do I see a history of everything my AI agent did today?
By the end of the day the chat has scrolled, the terminal is closed, and the agent's own summary is the only account of what it did. If it changed a file, sent a message, or spent money, you are taking its word for all of it. The fix is not a better summary. The fix is a history that was written while the work happened, by the system the work ran through.
The problem: agent history is written by the agent
Most agent setups keep history in three weak places: the conversation transcript, which mixes claims with actions; the tool logs, which live on one machine and scroll away; and the final summary, which the agent writes about itself. None of them let a second person check the day afterward. A history you can audit has to be recorded at call time, outside the agent's control, and readable later by someone who was not there.
How do I track what my AI agent is doing in real time?
Bind the work into a job and journal calls as they fire. In Zambo, an agent opens a job under one session id; every tool call made through the endpoint lands in that job's record immediately, with the tool name, the time, and a hash of the result. Nothing waits for the end of the session. If the agent stops halfway, the record of the half that ran is already there. Framework integrations journal automatically: the LangChain, CrewAI, Hermes, and twenty-framework integrations write each call to the job as it happens.
How do I log every action my AI agent takes?
Calls inside the job are journaled for you. Work the agent does somewhere else, a shell command, a payment, a message sent from another tool, gets added with a journal entry that names what happened and when. When the work finishes, one wrap-up seals the whole list into a verifiable receipt: a public page where anyone can open the job, see every step, and verify the saved result was not changed afterward.
A live example: one session, every call on record
This receipt is a real agent session, journaled live and wrapped when the session ended: https://zambo.dev/run/3d23c2f2-abcf-488e-ba0f-80e2eb9b4def. Four tool calls, each recorded as it ran, the finished job sealed into one AER-1 receipt. Open it and you are looking at the same history the agent's operator sees. Zambo has issued more than 108,000 of these receipts, and every one of them still verifies.
How do I see a history of everything my AI agent did today?
Ask the job record, not the agent. A day of work in Zambo is a list of jobs; each job is its steps; each finished job carries its receipt link. The history is per agent as well: receipts name the caller that ran them, so one agent's day can be read apart from another's. That is the difference between observability as a live chart and observability as a record: the record is still there, unchanged and checkable, when the auditor, the client, or the next AI arrives tomorrow.
Related
- AI agent handover: pass the job, not a summary
- AI agent observability: the record behind the chart
- How do I verify the identity of an AI agent?
- How do I evaluate whether my AI agent did a good job?
Questions, answered
How do I track what my AI agent is doing in real time?
Journal each tool call as it happens instead of reconstructing it later. In Zambo, a job binds the calls an agent makes under one session id, and every call lands in the job record the moment it runs, so the history exists while the work is still going, not after someone writes a summary.
How do I log every action my AI agent takes?
Route the work through one record. Calls made inside a Zambo job are journaled automatically; work an agent does outside the endpoint is added with a journal entry naming what happened. When the job wraps, the full list seals into a verifiable receipt anyone can open.
Best tools to monitor AI agent activity?
Dashboards show you what an agent claims. A receipt history shows you what you can check: each entry carries the tool, the time, and a hash of the result, and the finished job verifies against a public page. Monitoring tells you something moved; a verifiable history lets a second person confirm exactly what.
How do companies audit autonomous AI agents?
They keep a per-job record that survives the session: who ran it, which tools fired, what came back, and a receipt for the finished job that a client, a manager, or another AI can verify without trusting the agent's own report. Zambo issues that record as an AER-1 receipt for every job.
Run your first job free
Zambo is the verifiable work layer for AI: tools behind one MCP endpoint, continuity that carries work across AIs, and a verifiable AER-1 receipt for every job. Connect the endpoint at https://zambo.dev/mcp and the first 20 calls plus one wrap-up are free every day. Picking up a job in another AI is always free. A Day Pass is $0.99 for unlimited calls for 24 hours.