Sprint 7 closed: Engineering Agents
Sprint 7 is closed.
The goal was to introduce specialized engineering agents — architecture, code review, documentation, debugging, and onboarding — built on skills, memory, scanner output, and MCP. Agents do not call external LLMs; they gather project evidence and return structured briefings so the host model can execute the work with the right context and checklists.
What Sprint 7 delivered
Every item on the roadmap is checked off:
- Architecture agent — ADRs, repo layout, dependency boundaries
- Code review agent — git diff stat, optional GitHub PR context
- Documentation agent — doc gaps, GitHub doc draft excerpts
- Debugging agent — symptom-driven investigation with recent commits and diff
- Onboarding agent — vision, sprint, ADRs, and first safe commands
Product MCP is now v1.2.0 with agent_list and agent_run. Local MCP is v1.18.0 with the same agent surface.
New package: @meronq/agents
| Module | Role |
|---|---|
listAgents() / getAgent() |
Built-in agents (v0 — no project overrides yet) |
runAgent() |
Gather context and return AgentBriefing |
gatherAgentContext() |
Domain-specific context (architecture, PR, docs, debug, onboarding) |
Each briefing includes:
- context — scanner index, git, GitHub, doc excerpts
- checklist — ordered review or investigation steps
- systemGuidance — role instructions for the host LLM
- recommendedSkills — e.g.
project.test,adr.create - suggestedPrompts — example user intents
Execution context lives in @meronq/server-core (createAgentContextAsync) — same pattern as skills, without circular dependencies.
Surfaces
meronq agent list
meronq agent run onboarding
meronq agent run debugging --symptom "pnpm test fails in @meronq/skills"
meronq agent run code_review --pr 12 --focus security
MCP tools: agent_list, agent_run. Handshake returns available_agents[] alongside available_skills[].
agent_run is read-only in v0 — no runtime or file-write permission gates. Agents recommend skills; skills execute procedures.
Pipeline
Intent (agent name + inputs)
→ getAgent
→ createAgentContextAsync (index, git, GitHub, docs)
→ gatherAgentContext (domain-specific)
→ AgentBriefing (context + checklist + guidance)
→ Host LLM + recommended skills
Skills answer how to run build, test, or create an ADR. Agents answer when and why, with the evidence an assistant needs before acting.
Sprint 8 — Productization
Sprint 8 goal: turn prototypes into a usable product — desktop app exploration, cloud backend, team workspace, authentication, and billing.
Agents and skills are the intelligence layer. Productization makes that layer accessible beyond local MCP and CLI.
Follow along
Sprint 6 made procedures reusable. Sprint 7 gives AI clients specialized engineering roles with evidence-backed briefings.