AI agents | Tracking
AI agent tracking is following each agent from request to result, meaning every step it took, every tool it called, what each step cost and who approved what.
For each run, the request, the user it served, each step and tool call with its inputs and outputs, the model and prompt versions, time taken, tokens and cost, errors and retries, and any human approval. Give every run and step an ID so one task can be followed across agents and services.
Pass one trace ID through every agent that touches a task, and record which agent handed work to which. On TIP each agent signs its own steps with its own key, so the hand-offs can be attributed as well as seen.
Record tokens and tool charges on every step and roll them up per task, per user and per agent. Set limits an agent cannot exceed, because a loop that keeps calling a model can run up cost quickly. OWASP lists this risk as unbounded consumption.
OpenTelemetry publishes semantic conventions for generative AI, which name the attributes for model calls, token use and agent operations so different tools can read the same traces. Using them keeps you from being locked into one vendor's format.
Tracing and evaluation tools such as LangSmith, Langfuse and Arize Phoenix, and observability platforms such as Datadog, show what happened inside a run. They are built for engineers. TIP sits beside them and adds a record that people outside your team can verify.
Your own tracking data lives in systems your team controls, which is fine for debugging and weak as evidence. TIP adds a signed, time-stamped record of each action, linked to the step before it, that anyone you choose can check. Only a fingerprint of the data goes on the network.
Agent observability on TIP records each agent action as a signed, tamper-evident entry that anyone you choose can check, while your data stays on your systems. It is a commercial service from The AI Lab.
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