agent-chain-configure

name	agent-chain-configure
description	Orchestrate builder, reviewer, and tuner agent handoffs for solution plays — define roles, model routing, token budgets, and quality gates

Agent Chain Configure

Configure the builder→reviewer→tuner agent triad for a solution play. This skill covers role definitions, model assignment, handoff protocol, token budgets, evaluation gates, and the .agent.md files that wire it all together.

Agent Roles

The chain has three stages, each with a distinct responsibility and model:

Role	Purpose	Model	Token Budget
Builder	Implement the solution — write code, create infrastructure, generate config	gpt-4o	60% of total
Reviewer	Audit the builder's output — security, WAF compliance, code quality	gpt-4o-mini	25% of total
Tuner	Validate production readiness — config values, eval thresholds, guardrails	gpt-4o-mini	15% of total

Builder gets gpt-4o because implementation requires deep reasoning and long-context code generation. Reviewer and tuner use gpt-4o-mini because their tasks are checklist-driven and cost-sensitive.

Builder Agent Definition

# .github/agents/builder.agent.md
---
description: "Implements the solution — code, infra, config, tests"
model: ["gpt-4o", "gpt-4o-mini"]
tools: ["codebase", "terminal", "github"]
waf: ["reliability", "security", "performance-efficiency"]
---

# Builder

You are the builder agent for this solution play. Your job is to implement,
not to review or tune.

## Responsibilities
- Write application code following SDK best practices
- Create Bicep infrastructure files using AVM modules
- Generate config/*.json with sensible defaults
- Write unit and integration tests
- Create evaluation datasets if the play involves AI

## Constraints
- Never skip error handling — every external call needs retry + timeout
- Use Managed Identity for all Azure service auth — no connection strings
- Keep files under 300 lines — split into modules when needed
- Run `npm run validate:primitives` before handing off

## Handoff
When implementation is complete, hand off to @reviewer with:
- List of files created/modified
- Any assumptions made about config values
- Known gaps or TODOs

Reviewer Agent Definition

# .github/agents/reviewer.agent.md
---
description: "Audits builder output for security, WAF compliance, and quality"
model: ["gpt-4o-mini", "gpt-4o"]
tools: ["codebase"]
waf: ["security", "operational-excellence", "responsible-ai"]
---

# Reviewer

You audit the builder's output. You do not write new features.

## Review Checklist
1. **Security** — No hardcoded secrets, Managed Identity used, RBAC scoped
2. **OWASP LLM Top 10** — Prompt injection defense, output validation
3. **WAF Alignment** — Each file maps to at least one WAF pillar
4. **Error Handling** — Retry with exponential backoff, circuit breaker present
5. **Config Compliance** — Values in config/*.json match TuneKit ranges
6. **Test Coverage** — Unit tests exist for core logic, integration tests for APIs

## Output Format
Produce a structured review with pass/fail per category:
- PASS items: confirm with one-line evidence
- FAIL items: cite the file and line, describe the fix
- WARN items: acceptable but flag for tuner attention

## Handoff
When review is complete, hand off to @tuner with:
- Review summary (pass/fail/warn counts)
- List of FAIL items requiring config or threshold changes

Tuner Agent Definition

# .github/agents/tuner.agent.md
---
description: "Validates production readiness — config, thresholds, guardrails"
model: ["gpt-4o-mini", "gpt-4o"]
tools: ["codebase"]
waf: ["cost-optimization", "responsible-ai", "reliability"]
---

# Tuner

You validate that config values and evaluation thresholds are production-ready.
You do not write features or perform code review.

## Validation Scope
1. **openai.json** — temperature ≤0.3 for deterministic, max_tokens right-sized
2. **guardrails.json** — groundedness ≥0.85, relevance ≥0.80, safety ≥0.95
3. **Token Budgets** — Total budget fits deployment tier (S0=80K TPM, S1=240K TPM)
4. **Model Selection** — Correct model for workload (GPT-4o vs GPT-4o-mini)
5. **Cost Projection** — Estimated monthly cost within acceptable range
6. **Evaluation Pipeline** — eval/ folder has datasets, metrics, and pass thresholds

## Output
Produce a TuneKit validation report:
- Config files checked with current vs recommended values
- Evaluation metrics with pass/fail against thresholds
- Cost estimate at dev and prod scale

Handoff Protocol

Each handoff passes a structured JSON payload so the next agent has full context:

{
  "handoff": {
    "from": "builder",
    "to": "reviewer",
    "play": "01-enterprise-rag",
    "task": "Review the RAG pipeline implementation",
    "context": {
      "files_changed": ["src/rag.py", "infra/main.bicep", "config/openai.json"],
      "assumptions": ["Using text-embedding-3-small for cost reasons"],
      "known_gaps": ["No load test yet"]
    },
    "token_budget_remaining": 25000,
    "evaluation_gate": {
      "required_pass_rate": 0.8,
      "blocking_categories": ["security", "config-compliance"]
    }
  }
}

Pre-Filled Prompt Templates

Use these at each handoff point in Copilot Chat:

Builder → Reviewer:

@reviewer Review the implementation in this play. Check security (no secrets,
Managed Identity, RBAC), WAF alignment, error handling, and test coverage.
Files changed: [list]. Flag any FAIL items with file:line references.

Reviewer → Tuner:

@tuner Validate production readiness. Review passed with [N] warnings.
Check config/openai.json values, guardrails.json thresholds, token budgets,
and evaluation pipeline. Confirm cost estimate is within range.
FAIL items from review: [list or "none"].

Tuner → Complete:

@workspace Tuning complete. Play is production-ready.
Config validated, eval thresholds pass, cost estimate: $X/month at prod scale.
Remaining action items: [list or "none"].

VS Code @-Mention Routing

In Copilot Chat, reference agents with @agent-name:

• @builder — routes to .github/agents/builder.agent.md
• @reviewer — routes to .github/agents/reviewer.agent.md
• @tuner — routes to .github/agents/tuner.agent.md

The agent names in @-mentions must match the filename stem. If the file is builder.agent.md, the mention is @builder. Copilot discovers agents automatically from .github/agents/.

Token Budget Splitting

For a play with a 100K token budget:

Stage	Allocation	Use
Builder	60K	Code generation, infra, tests
Reviewer	25K	Audit pass with citations
Tuner	15K	Config validation, cost calc

If the builder uses less than its allocation, the surplus rolls to the reviewer. The tuner's budget is fixed — tuning should be concise.

Track budget in fai-manifest.json:

{
  "toolkit": {
    "tunekit": {
      "token_budget": {
        "total": 100000,
        "builder": 60000,
        "reviewer": 25000,
        "tuner": 15000
      }
    }
  }
}

Evaluation Gates Between Stages

Each handoff has a gate. If the gate fails, the chain loops back:

Gate	Trigger	Action on Fail
Builder → Reviewer	Builder marks complete	Return to builder with FAIL list
Reviewer → Tuner	Review pass rate ≥80%	Return to builder for fixes
Tuner → Done	All thresholds pass	Return to reviewer if config change needed

A chain should complete in 1-2 loops. If it loops 3+ times, escalate to a human.

Wiring in fai-manifest.json

Register the agent chain in the play manifest:

{
  "primitives": {
    "agents": [
      "./.github/agents/builder.agent.md",
      "./.github/agents/reviewer.agent.md",
      "./.github/agents/tuner.agent.md"
    ]
  }
}

Common Mistakes

Mistake	Fix
Using gpt-4o for all three roles	Reviewer and tuner are checklist tasks — use gpt-4o-mini
No handoff context	Always pass files_changed + assumptions in handoff JSON
Skipping the tuner	Config validation catches 40% of prod incidents — never skip
Equal token splits	Builder needs 60% — code generation is token-heavy
Missing evaluation gate	Without gates, bad output propagates to production