AgentEM builds custom AI agents that handle specs, risk detection, review routing, and sprint management — so your team ships instead of coordinates.
A senior engineer writes a spec missing an ADR constraint — rework costs a week. A PR sits unreviewed for 3 days because nobody knew who owned that service. A risk surfaces on Thursday that was visible on Monday. The sprint ends 30% short because capacity planning was a guess. None of this is an engineering problem. It's an operational visibility problem.
We interview your engineering leader to extract the implicit knowledge that makes your team run — architecture decisions, real review standards, actual capacity numbers, what burned you last quarter. This becomes structured context files that the agent reads before every run.
We build custom skills wired to your tools — GitHub, Linear, Jira, Slack, Figma. Each client gets a different configuration based on their architecture, team topology, and standards. We run the first full cycle and tune against real data.
The first 2–3 sprint cycles require threshold tuning. After that, the retro analyzer feeds learnings back into context files, and the system improves itself every sprint. You graduate from approving everything to approving only what matters.
Each skill reads your context files — your architecture, your team, your standards — before every run. The output isn't generic. It's yours.
Produces implementation specs that know your ADRs, your system architecture, and your team's actual capacity constraints.
Breaks specs into right-sized tickets with acceptance criteria, effort estimates, and dependency mapping based on your team topology.
Continuous scan for stale PRs, blocked tickets, scope creep, capacity overload, and cross-team dependency risks.
Reads the diff, checks code ownership and reviewer load, assigns the right reviewers, and posts a structured PR summary.
Tracks what's shipping, checks readiness gates, flags missing items, and generates release notes from merged PRs.
Generates sprint retrospectives from real data — velocity, review cycles, risk accuracy — and proposes context file updates that make the next sprint better.
1,400 lines of architecture, context file templates, all 6 skill definitions, database schema, and build instructions. Drop it into your repo, point Claude Code at it, and start building.
Download .mdThe blueprint gets you started. We build the autonomous layer when you're ready.
The full engineering agent blueprint. Drop into Claude Code and build it yourself.
Context extraction workshop plus agent build. For teams of 5–15 engineers.
Custom agents, full integrations, autonomous runtime, 3-sprint tuning cycle. For teams of 15–50.
Hosted agents, ongoing tuning, multi-team support. For growth-stage teams of 50+.
30-minute call. We'll map your team's workflow and show you what the agent would look like for your engineering org.
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