Coding Agent Observability
See the coding-agent work that happens before a pull request, ticket, or review: prompts, commands, retries, files, decisions, and unresolved loops.
// who it helps
Engineering teams using local coding agents for real software work.
// the painful moment
Why this matters
Most agent activity happens in local sessions that disappear from the systems leaders already trust. By the time a PR exists, the prompts, failed attempts, shell output, and decisions that shaped the work are hard to reconstruct.
What AgentPM captures
- Human prompts, agent replies, tool calls, terminal commands, and command output.
- Files, repos, directories, and workspaces connected to each session.
- Extracted keyframes such as decisions, artifacts, risks, state changes, and open loops.
What teams can do
- Review agent-assisted work with evidence instead of memory.
- Find repeated friction across repos, tools, and developers.
- Turn successful agent workflows into repeatable team patterns.
Questions this answers
- What did agents actually work on this week?
- Where did agents get stuck or retry the same task?
- Which decisions happened before a PR or ticket existed?
// fit
What AgentPM is not replacing
AgentPM does not replace LLM observability, GitHub, Jira, Linear, or code review. It captures the work history those systems usually never see.
A typical workflow
- 01Install the local collector on a developer machine.
- 02Route Claude Code, Codex, Cursor, and Grok sessions into the right organization.
- 03Search sessions, inspect transcript evidence, and review extracted keyframes.
- 04Use the evidence to improve prompts, playbooks, review habits, and rollout decisions.
// common questions
Questions about coding agent observability
What is coding agent observability?
Coding agent observability is visibility into the software work humans and agents do together: prompts, agent replies, terminal output, file changes, retries, decisions, and unresolved work before those details reach a PR or ticket.
How is AgentPM different from LLM observability?
LLM observability usually focuses on model calls, traces, cost, latency, and evals. AgentPM focuses on engineering work: what the agent attempted, what changed, where it got stuck, and what evidence explains the session.
What is AgentPM?
AgentPM is an evidence layer for coding-agent work. It captures local agent sessions, makes them searchable, and helps teams understand what happened before work becomes a pull request, ticket, or review.
How is AgentPM different from LLM observability?
LLM observability usually tracks model calls, traces, cost, latency, and evals. AgentPM tracks engineering work: what agents attempted, where they got stuck, what changed, and what evidence explains the session.
Does AgentPM replace GitHub, Jira, or Linear?
No. AgentPM captures the work that happens before those systems have a clean artifact. It complements code hosts, issue trackers, and review tools with searchable session evidence.
// related
Related use cases
Search
Agent Session Search
Search across coding-agent sessions to find prior decisions, repeated failures, terminal output, implementation attempts, and reusable workflow patterns.
Evidence
Agent Audit Trail
Keep a reviewable audit trail of agent-assisted software work: what was asked, what ran, what changed, what failed, and what remained unresolved.
Coaching
Agent Workflow Coaching
Use real coding-agent sessions to spot repeated friction, planning misses, skill gaps, and reusable practices your team can improve next week.