step 01
Capture local agent work
Install one collector per machine and route Claude Code, Codex, Cursor, and Grok sessions into the right organization.
PRIVATE BETA
See what your coding agents actually do.
AgentPM gives developers and teams a shared evidence layer for Claude Code, Codex, Cursor, Grok, and other coding-agent work so they can achieve more, faster.
# work-observability# cross-agent-evidence# team-learning-loops
agentpm.dev/orgs/350-nice/overview
350-NICE / OVERVIEW
Activity this sprint
▦ Activity heatmapsessions / day
billing-svc
web-app
infra
lessmore
◎ Coaching signalsauto-extracted
!Planning skipped on payments refactorrepo: billing-svc · 4 sessions · high churn×4
↻Repeated retry loop on flaky auth testrepo: web-app · costs ~22 min/run×7
✦Reusable pattern: migration + backfillworth codifying to CLAUDE.mdnew
// the missing layer
Coding agents changed how software gets built. The evidence layer is missing.
01
Agent work happens before GitHub, Jira, or Linear.
A feature can span prompts, plans, tool calls, retries, terminal output, file edits, and half-finished fixes before it ever becomes a PR or ticket.
02
Teams cannot improve what they cannot see.
Developers, PMs, and engineering leaders need to know what agents attempted, where they got stuck, what changed, and which patterns are helping or slowing the team down.
03
AgentPM turns agent sessions into searchable evidence.
See how your team actually uses coding agents, then use that evidence to ship more, review faster, reduce risk, and build better agent workflows.
// the difference
Not another dashboard for model calls.
Most AI tooling looks at prompts, traces, evals, or app performance. AgentPM focuses on the actual software work humans and agents do together.
// other tools answer
- ✕Which model call failed?
- ✕How much did this workflow cost?
- ✕Did the agent pass an eval?
// agentpm answers
- ✓What did agents actually work on?
- ✓What changed, broke, or got left unfinished?
- ✓How can this team use agents better next week?
Bottom line: AgentPM is work observability for human-agent software teams.
// what it does
AgentPM makes invisible agent work inspectable.
step 02
Summarize the PM signal
Find decisions, artifacts, state changes, risks, and implementation moments without reading every token.
step 03
Coach from real evidence
Review planning misses, skill gaps, and repeatable patterns with references back to the exact transcript turns.
// from session to evidence
Four steps from raw agent session to engineering evidence.
Read the setup guide01 — connect
Connect your first agent workspace
Install the local collector with one org-scoped command. AgentPM routes sessions to the right workspace from the first run.
02 — observe
Watch agent activity land in real time
See active directories, session heatmaps, search, and coaching signals across every connected developer machine.
03 — review
Review the full session record
Keep prompts, agent replies, commands, tool output, files, and decisions together in one inspectable notebook.
04 — learn
Turn transcripts into team intelligence
AgentPM converts long agent sessions into keyframes, risks, open loops, and concrete coaching signals your team can act on.
// who it's for
For teams already using agents to ship real work.
@developers
Developers shipping with agents
Review your own sessions, find repeated friction, and turn successful workflows into reusable patterns.
@eng-leads
Engineering leads coordinating agent work
See where agent-assisted work is happening across repos, machines, projects, and tools — without asking everyone to reconstruct their day.
@product
Product and technical leaders
Understand what agents changed, missed, decided, or left unresolved before the context disappears into chat history and terminal logs.
Ready to see what your agents are actually doing?
Start with one org and one developer machine. Capture agent sessions, trace work from prompt to PR, and see where time, risk, and context are getting lost.
$curl -fsSL 'https://agentpm.dev/install.sh?org=…' | bashcopy