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GRPNR.

Orchestration, Triage Gateway, and Guardrails

Multi-Agent Orchestration App (Temporal + LangGraph)

  • Up to 1,000 parallel agents: Temporal durable workflow engine (distributed control plane) + LangGraph graph-based state runtime (local agent loop).
  • Git worktrees dynamically provisioned per agent thread — eliminates concurrent file-modification conflicts, keeps changes separated.
  • Plan Mode discipline: high-capability model in a planning container analyzes service dependencies, compiles explicit execution playbook (plan.md) — ordering constraints, transactional boundaries, explicit human-escalation conditions. After plan validation, code modification delegated to smaller/faster/cheaper executor models.
  • Prevents sequencing errors: e.g. writing DB columns before updating insertion logic; treating shared DB tables as independently owned by an extracted microservice.

Two-layer structure:

LangGraph local loop:  Analyze Code → Write Code → Run Compiler →(error? loop)→ Execute Tests
        ↓ durable step transition
Temporal control plane: Activity 1 (local agent run) → Activity 2 (triage gate) → Activity 3 (system rollout)
        transactional durability · replay memory ledger · fault isolation

Intelligent Question Triage & Routing App

At 1,000 agents, clarifying-question volume overwhelms teams. Gateway between fleet and humans:

  1. Intercept + standardize every agent query into structured JSON: target microservice, file paths, proposed change, calculated confidence score.
  2. Semantic deduplication: vector embeddings of question text, real-time clustering. New question matches active cluster → auto-apply previous human decision (resolves similar blocks across microservices).
  3. Dynamic risk scoring for unique questions: low-risk (score < 0.70) → automated checkpointer gates; high-risk (≥ 0.70: DB migrations, authz changes) → human stakeholders.
  4. Ownership routing: map microservice → owner via enterprise directory; deliver concise triage alert via Slack/Jira — side-by-side AST-based diff, trade-off summary, risk score, one-click approve/edit/reject.

Measured effect: human interruptions reduced from 8 per task to 3 checkpoints.

Autonomous Quality & Security Guardrail App

Automated gate inside agent execution loop + CI/CD:

  • Code Health score (1–10) via MCP server to a code-health platform (CodeScene-style): structural complexity, nesting depth, class size. Agent must iterate in local validation loop until file reaches ≥9.5 “AI-ready” before commit.
  • Per-PR scans:
    • Semgrep + OSV-Scanner — security vulnerabilities, outdated dependencies
    • jscpd — copy-paste duplication
    • Anti-pattern checkers — TypeScript any abuse, “comment floods” (excessive model-added comments), “ghost files” (redundant files from incomplete context)
  • Agent-generated PRs still route through human reviewers for final approval: unguided code-review-agent comments are noisy (60.2% fall in a 0–30% signal range) and raise reviewer cognitive load — combine automation with human architectural judgment.