Orchestrating AI Agent Teams Like Real Projects

Purpose: Stop context loss, duplicated work, and broken handoffs by running AI agents with the same rigor you use for human teams—clear roles, formal handoff contracts, and tight observability.

Core Principles

One Source of Truth
- Project Charter: state objective, scope, owner, KPIs.
- Artifact Store: versioned prompts, configs, outputs, and logs.
- RACI for agents: Responsible (agent), Accountable (human), Consulted, Informed.
Hard Handoff Rules
- Every handoff is a contract: required inputs → validation → schema-compliant output.
- Prefer JSON Schemas and validate before passing work forward.
- Define SLAs, retries with backoff, and a time-to-handoff KPI.
Context Management
- Short-term context lives with the task/ticket.
- Long-term knowledge in a KB (FAQ, policies, product data).
- Minimize context windows: summaries + references instead of dumping full docs.
Eliminate Duplicate Work
- Use a dispatcher/queue with locks and idempotency keys.
- Deduplicate at intake (hash by task content and constraints).
Governance & Safety
- Guardrails (PII masking, RBAC, policy checks) at entry and exit points.
- Observability: trace IDs, metrics (success, latency, cost), and a complete audit log.
- Human-in-the-loop on consequential steps (approvals, exceptions).

Reference Architecture

Intake Layer: form/email/webhook → normalize to a single JSON task model.
Router/Orchestrator: rule engine + LLM router assigns the optimal path.
Workers (Agents): small, precise skills (CRUD, enrichment, text, code).
Memory: task-state DB (short-term) + vector/KB (long-term).
Event Bus: publish/subscribe for results and downstream triggers.
Control Room: dashboard for runs, costs, queues, and handoffs.

Practical Templates

1) Agent RACI (fillable)

{
  "agent_name": "…",
  "purpose": "…",
  "input_schema_ref": "schemas/intake.v1.json",
  "output_schema_ref": "schemas/out.v1.json",
  "raci": {
    "Responsible": "agent.x",
    "Accountable": "owner.y",
    "Consulted": ["agent.z"],
    "Informed": ["stakeholder.a", "stakeholder.b"]
  }
}

2) Handoff Contract (JSON Schema)

{
  "type": "object",
  "required": ["task_id", "source_agent", "target_agent", "payload"],
  "properties": {
    "task_id": { "type": "string" },
    "source_agent": { "type": "string" },
    "target_agent": { "type": "string" },
    "sla_seconds": { "type": "integer", "minimum": 1 },
    "payload": { "$ref": "schemas/payload.v1.json" },
    "validation_rules": { "type": "object" }
  }
}

3) Runbook (Incidents & QA)

Detection: alert when error rate > X% or p95 latency > Y s.
Triage: pinpoint the failing handoff, schema drift, or regression.
Rollback: revert to the last stable prompt/config.
Learning: update KB, add tests, harden guardrails.

KPIs That Matter

Handoff Success Rate: % of valid transfers.
Cycle Time per Task: intake → outcome.
Cost per Resolved Task: currency per completed run.
First‑Pass Yield: % completed without human edits.
Duplicate Suppression Rate: prevented duplicates vs. attempts.

Quick Start (≈ 90 minutes)

Define one objective + one KPI (e.g., qualify leads < 10 min, FPY ≥ 80%).
Build three agents: intake → enrichment → summary/action.
Lock down JSON schemas for every handoff and validate strictly.
Log everything (task_id, cost, latency, status).
Stand up a small dashboard: 5 KPIs + retry/approve buttons.

Common Pitfalls

Monolithic “super‑agents” instead of small, reliable workers.
Prompt changes without versioning → silent regressions.
Hidden context in local notes rather than a shared KB.
No idempotency → duplicated emails, invoices, or posts.

Applying This Playbook

Public Tenders

Intake (criteria) → Evidence matcher (cases/references) → Draft writer (per section) → QA checker (compliance vs. dossier).

ATS Pipelines

Vacancy normalization → Skill/ISCO enrichment → Matching → Outreach with dedup + consent checks.

CRO & Content

Brief → Generation → Editorial lane → Legal/brand guardrails → Publish with version tags.

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