InficyAI agent observability
Application observability was built for services that answer requests. Agents are different: they take a sequence of actions, use tools and services, retry, get interrupted and sometimes stop halfway. Inficy records that sequence and makes it readable.
What AI agent observability means here
Observability for agents means being able to answer, after the fact and without guessing: what the agent was asked to do, which steps it took and in what order, which models, tools, services and sub-agents were involved, where the time went, what failed or repeated, and what was never captured.
What Inficy shows for each execution
- The instruction that started the run, as the runtime received it.
- Every step in execution order, with its own start, end and outcome.
- Models, tools, services and sub-agents attributed to the step that used them.
- Where activity time went, kept separate from wall-clock duration.
- Failures, retries, interruptions and recoveries, and human interventions.
- Capture coverage: facts the runtime did not report stay unavailable, never zero.
Monitoring without a false picture
Most agent dashboards fill gaps with defaults. A missing token count becomes 0, a missing duration becomes 0 ms, and the averages quietly lie. Inficy separates measured facts from absent ones, so a coverage gap shows up as a coverage gap.
Runtimes you can observe today
Codex CLI, Claude Code and Hermes are observed at runtime and preserved as canonical NexArt Project Bundles. ChatGPT sessions connect over MCP and are agent-reported checkpoints: useful for tracking activity, labelled as such everywhere because Inficy did not observe the underlying execution.
Related reading
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