Leaderboard System Design AGENTS.md Template

Overview

Direct answer: This AGENTS.md Template defines the operating context for AI coding agents to implement a leaderboard system design, including a clear multi-agent orchestration pattern, handoffs, and governance rules. It provides both an individual-agent operating model and a scalable, orchestrated workflow that preserves memory, source-of-truth, and auditable decisions across ranking, data ingestion, validation, and publish steps.

In practice, the template unifies single-agent execution with multi-agent orchestration by specifying an agent roster, supervisor behavior, and explicit handoff rules. It ensures tool governance, memory handling, and versioned artifacts so that every leaderboard update can be traced, reproduced, and reviewed by humans when needed.

When to Use This AGENTS.md Template

• When designing a leaderboard system that requires reliable multi-agent collaboration (ingest, rank, validate, and publish).
• When you need explicit handoffs, versioned context, and a single source of truth for leaderboard data.
• When adopting governance and security controls around data access, secrets, and production changes.
• When you require testable deployment, rollback paths, and human review in critical steps.

Copyable AGENTS.md Template

# AGENTS.md

Project role
- Leaderboard System Design using AI coding agents. The project role defines the objective: generate and maintain a fair, timely leaderboard from event data and score updates, with auditable agent decisions.

Agent roster and responsibilities
- Planner Agent: decomposes the overall leaderboard task, sequences work, and maintains plan provenance.
- DataIngest Agent: ingests raw events, score updates, and activity logs into the canonical data store; normalizes data and deduplicates.
- Ranking Agent: computes rankings, resolves ties, and updates ranking artifacts; enforces ranking rules and fairness constraints.
- Validation Agent: validates input data, computed rankings, and artifact integrity; reports anomalies for remediation.
- Handoff Orchestrator: central controller that coordinates transitions between agents, preserves memory, and enforces governance rules.
- Domain Specialist Agent (optional): adjusts ranking criteria for domain-specific requirements.
- Reviewer Agent: reviews the final leaderboard artifact before publication.
- Tester Agent: runs test scenarios and verifies end-to-end correctness.

Supervisor or orchestrator behavior
- The Orchestrator monitors outputs, stores context to memory, coordinates agent handoffs, and records provenance.
- It enforces memory, source-of-truth, and versioning; triggers the next agent when criteria are met; handles failure and escalation.

Handoff rules between agents
- Ingest → Rank: after data quality is verified and normalized; store a memory checkpoint and version.
- Rank → Validate: once ranking artifacts are produced, pass to validation with context preserved.
- Validate → Reviewer: if validation passes, proceed to final review; otherwise rework with the planner.
- Reviewer → Orchestrator: after approval, orchestrator publishes to production or triggers a deployment pipeline.

Context, memory, and source-of-truth rules
- All outputs reside in a versioned memory store; source-of-truth is the canonical leaderboard database.
- Agents reference memory by IDs; memory is immutable for past artifacts and mutable for current work.
- Do not use stale data; timestamp all actions and attach lineage to artifacts.

Tool access and permission rules
- Agents access only required tools and APIs; adopt least privilege principals.
- Secrets retrieved from a secure vault; never hard-code credentials.
- Production changes require explicit approval and a deploy gate.

Architecture rules
- Event-driven, modular architecture with clear boundaries (ingest, compute, validation, publish).
- Agents are stateless; memory and provenance live in a centralized store.
- All communications are auditable and idempotent where possible.

File structure rules
- directory tree should be lean and workflow-focused; no unrelated folders.

Data, API, or integration rules when relevant
- Data schema example: { user_id, score, event_time, version, source }
- Use versioned API contracts for producer/consumer boundaries.

Validation rules
- Check required fields, types, non-null constraints, and version coherence.
- Validate data against business rules for leaderboard fairness.

Security rules
- Enforce role-based access controls; rotate credentials; audit all access and changes.

Testing rules
- Unit tests per agent; integration tests for end-to-end leaderboard updates; tests cover failure and rollback scenarios.

Deployment rules
- Canary or blue/green releases; monitor metrics; rollback on degradation.

Human review and escalation rules
- Escalate critical failures to on-call engineers; require human review for rule changes or ranking policy updates.

Failure handling and rollback rules
- If any step fails, roll back to the last valid artifact; record rollback actions; trigger compensating workflow as needed.

Things Agents must not do
- Do not modify production data without authorization; do not bypass validation or security controls; do not share memory across unrelated tasks.

Recommended Agent Operating Model

The recommended operating model defines clear roles, decision boundaries, and escalation paths for single-agent and multi-agent runs. Planner maintains the overall strategy; each specialized agent handles its domain; the Orchestrator ensures correct sequencing, memory sharing, and auditable handoffs. If a decision requires human judgment, the path to escalation is explicit and traceable.

Recommended Project Structure

leaderboard-system-design/
├── orchestrator/
│   └── README.md
├── agents/
│   ├── ingest_agent/
│   │   ├── __init__.py
│   │   └── main.py
│   ├── ranking_agent/
│   │   ├── __init__.py
│   │   └── main.py
│   ├── validation_agent/
│   │   ├── __init__.py
│   │   └── main.py
│   ├── reviewer_agent/
│   │   ├── __init__.py
│   │   └── main.py
│   └── domain_specialist_agent/
│       ├── __init__.py
│       └── main.py
├── data/
│   ├── raw/
│   └── curated/
├── rules/
│   ├── memory.md
│   └── access_control.md
├── tests/
│   ├── unit/
│   └── integration/
└── docs/
    └── overview.md

Core Operating Principles

• Clear ownership: every agent has a defined role, responsibility, and decision boundary.
• Idempotent operations: repeated executions do not change the outcome beyond the first execution.
• Single source of truth: memory and provenance are centralized and versioned.
• Explicit handoffs: every handoff is versioned, validated, and auditable.
• Security by default: least privilege, secrets management, and auditable changes.
• Observability: metrics, traces, and logs support troubleshooting and governance.

Agent Handoff and Collaboration Rules

Rules by role ensure predictable collaboration and minimize drift:

• Planner → Ingest: planner seeds tasks and constraints; ingest validates inputs before absorbing data.
• Ingest → Ranking: upon successful ingestion, share normalized data and quality signals.
• Ranking → Validation: publish ranking artifacts and context for validation checks.
• Validation → Reviewer: if valid, pass for final review; otherwise, route back to Planner with remediation notes.
• Domain Specialist → Ranking: apply domain-specific ranking adjustments; ensure traceability of changes.
• All handoffs must include memory snapshot, artifact version, and provenance.

Tool Governance and Permission Rules

• Execute only approved commands and API calls; respect rate limits and quotas.
• Do not access production secrets without a gated approval flow; use a vault for secrets.
• Changes to the ranking logic require an approval gate and unit/integration tests before deployment.
• Logs should never contain secrets; redact sensitive fields when possible.

Code Construction Rules

• Code must be modular, readable, and well-documented; all functions must be pure where feasible.
• Use explicit versioning for artifacts; avoid in-place destructive changes without a rollback plan.
• Tests must cover edge cases in ranking, data ingestion, and validation paths.
• Avoid hard-coded values; rely on configuration and environment-driven behavior.

Security and Production Rules

• Enforce role-based access controls; rotate credentials; monitor for anomalous activity.
• Apply data governance rules to prevent leakage of sensitive data through artifacts.
• Maintain a rollback plan and incident response procedure for production issues.

Testing Checklist

• Unit tests for each agent’s core logic.
• Integration tests for end-to-end leaderboard update scenarios.
• Regression tests for handoffs and memory propagation.
• Canary tests for production-like data volumes.

Common Mistakes to Avoid

• Omitting explicit handoffs or memory propagation leads to context drift.
• Over-privileged tool access or secret exposure in artifacts.
• Unversioned artifacts that make rollbacks impossible.
• Ambiguous ownership and unclear escalation paths.

Related implementation resources: AI Use Case for Sales Pipeline Reviews and Deal Risk Scoring and AI Use Case for Corporate Event Managers Using Slack To Orchestrate Day-Of Venue Tasks Across Multi-Department Teams.

FAQ

What is the purpose of this AGENTS.md Template for leaderboard design?

To standardize agent roles, handoffs, and governance for a leaderboard workflow using AI coding agents.

Who should use this template?

Product teams, platform engineers, and AI developers implementing leaderboard features with multi-agent orchestration.

How do agents hand off work in the leaderboard workflow?

Handoffs are explicit and versioned; memory and context are preserved in a shared source-of-truth; the orchestrator triggers the next agent when criteria are met.

What safeguards ensure data integrity and security?

Access controls, secret management, audit logs, and validation steps ensure data integrity and restricted tool usage.

What are common failure modes and rollback rules?

If a step fails, revert to the last valid artifact, notify the orchestrator, and trigger compensating actions; escalate for human review if needed.