Project 10: Capstone - Production Multi-Agent System

Build an end-to-end multi-agent system that integrates roles, memory, safety, observability, and evaluation.

Quick Reference

Attribute Value
Difficulty Level 5
Time Estimate 30-40 hours
Language Python (Alternatives: TypeScript, Go)
Prerequisites Projects 1-9 recommended
Key Topics System integration, reliability, observability

1. Learning Objectives

By completing this project, you will:

  1. Integrate orchestration, memory, and safety into one system.
  2. Add observability dashboards and audit trails.
  3. Validate outputs using a full evaluation harness.
  4. Demonstrate resilience under failure scenarios.

2. Theoretical Foundation

2.1 Core Concepts

  • System Integration: Aligning components with stable interfaces.
  • Reliability: Predictable behavior under stress and failure.
  • Operational Readiness: Observability, testing, and fallback plans.

2.2 Why This Matters

A multi-agent system is only valuable if it is trustworthy. This project makes reliability and observability first-class features.

2.3 Historical Context / Background

Production distributed systems have long required strong observability and testing. Agentic systems inherit these needs.

2.4 Common Misconceptions

  • “Integration is just wiring.” It requires stable contracts and monitoring.
  • “Testing once is enough.” Reliability requires continuous evaluation.

3. Project Specification

3.1 What You Will Build

A production-style multi-agent system that accepts real tasks, coordinates multiple roles, validates shared memory, enforces tool safety, and produces evaluation reports.

3.2 Functional Requirements

  1. Orchestrator: Role-based routing and escalation.
  2. Shared Memory: Validated knowledge ledger.
  3. Safety Gatekeeper: Tool-use policy enforcement.
  4. Observability: Logs, traces, and dashboards.
  5. Evaluation Harness: Automated test suite.

3.3 Non-Functional Requirements

  • Reliability: Handles failure gracefully.
  • Scalability: Supports multiple tasks.
  • Auditability: Every decision traceable.

3.4 Example Usage / Output

$ run-capstone --task "Market research with risk analysis"

[Orchestrator] task routed to 4 agents
[Memory] ledger updated (v12)
[Gatekeeper] tool request approved
[Evaluation] pass_rate=80%

3.5 Real World Outcome

A full demo where stakeholders can see a complete report, evidence ledger, safety approvals, and a final result with trace logs.

4. Solution Architecture

4.1 High-Level Design

User Task
  -> Orchestrator
     -> Agents
        -> Shared Memory
     -> Gatekeeper
  -> Observability + Evaluation
  -> Final Output

4.2 Key Components

Component Responsibility Key Decisions
Orchestrator Task routing Role contracts
Memory Ledger Store facts Append-only + validation
Gatekeeper Tool policy Risk-based approvals
Dashboard Observability Metrics and traces
Evaluation Harness Testing Scenario suites

4.3 Data Structures

Pseudo-structures:

STRUCT TaskContext:
  task_id
  roles
  evidence
  status

STRUCT SystemMetric:
  name
  value
  timestamp

4.4 Algorithm Overview

End-to-End Workflow

  1. Task enters orchestrator.
  2. Agents execute with validation gates.
  3. Gatekeeper approves tool use.
  4. Memory ledger updated.
  5. Evaluation harness validates results.

Complexity Analysis:

  • Time: O(T * R) tasks times roles
  • Space: O(L) logs and memory entries

5. Implementation Guide

5.1 Development Environment Setup

Use a staged environment to run integration tests before demo.

5.2 Project Structure

project-root/
├── orchestrator/
├── agents/
├── memory/
├── gatekeeper/
├── observability/
├── evaluation/
└── reports/

5.3 The Core Question You’re Answering

“How do I build a multi-agent system that I would trust in production?”

5.4 Concepts You Must Understand First

  1. Integration contracts
    • How do you prevent schema drift?
    • Book Reference: “Designing Data-Intensive Applications” - Ch. 1-5
  2. Operational safety
    • How do you handle failures?
    • Book Reference: “Release It!” - Ch. 4

5.5 Questions to Guide Your Design

  1. Component boundaries
    • Where do you enforce validation?
  2. Monitoring
    • What metrics indicate system health?

5.6 Thinking Exercise

Design a failure scenario where one agent returns nonsense and explain how the system recovers.

5.7 The Interview Questions They’ll Ask

  1. “What makes an agent system production-ready?”
  2. “How do you ensure observability?”
  3. “How do you handle systemic failures?”
  4. “What is your fallback strategy?”
  5. “How do you measure ROI for multi-agent systems?”

5.8 Hints in Layers

Hint 1: Start with a minimal pipeline Integrate orchestrator and memory first.

Hint 2: Add safety layer Introduce gatekeeper and policy checks.

Hint 3: Add observability Log tasks and metrics early.

Hint 4: Add evaluation Run harness before demo.

5.9 Books That Will Help

Topic Book Chapter
Reliability “Release It!” Ch. 4
Data systems “Designing Data-Intensive Applications” Ch. 1-5

5.10 Implementation Phases

Phase 1: Foundation (8-10 hours)

Goals:

  • Integrate core orchestration
  • Add memory ledger

Tasks:

  1. Wire role contracts
  2. Store validated outputs

Checkpoint: End-to-end task completes.

Phase 2: Core Functionality (10-12 hours)

Goals:

  • Add gatekeeper
  • Add observability

Tasks:

  1. Enforce tool policies
  2. Add logs and metrics

Checkpoint: Policies enforced and logs visible.

Phase 3: Polish & Edge Cases (10-12 hours)

Goals:

  • Add evaluation harness
  • Handle failures

Tasks:

  1. Run adversarial tests
  2. Add fallback responses

Checkpoint: System survives failure scenarios.

5.11 Key Implementation Decisions

Decision Options Recommendation Rationale
Integration order Big bang vs staged Staged Reduces risk
Monitoring depth Minimal vs detailed Detailed Debuggability

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit Tests Component checks Role validation
Integration Tests End-to-end runs Task flows through system
Edge Case Tests Failure scenarios Agent returns nonsense

6.2 Critical Test Cases

  1. Tool call blocked by policy.
  2. Conflicting memory updates flagged.
  3. Evaluation harness reports failures.

6.3 Test Data

Scenario: Missing evidence
Expected: output rejected

7. Common Pitfalls & Debugging

7.1 Frequent Mistakes

Pitfall Symptom Solution
Schema drift Integration fails Freeze contracts
Missing monitoring No visibility Add metrics early
No fallback System stops Add safe defaults

7.2 Debugging Strategies

  • Trace tasks end-to-end with task IDs.
  • Run evaluation harness after each change.

7.3 Performance Traps

  • Too many validation steps can slow throughput; tune thresholds.

8. Extensions & Challenges

8.1 Beginner Extensions

  • Add a demo scenario library.
  • Add a summary report.

8.2 Intermediate Extensions

  • Add cost tracking per task.
  • Add role performance analytics.

8.3 Advanced Extensions

  • Add distributed execution.
  • Add autoscaling policies.

9. Real-World Connections

9.1 Industry Applications

  • Production agentic workflows
  • Compliance-sensitive automation
  • LangGraph (agent workflow orchestration)
  • OpenTelemetry (observability)

9.3 Interview Relevance

  • End-to-end system integration is a common senior-level topic.

10. Resources

10.1 Essential Reading

  • “Designing Data-Intensive Applications” - integration patterns
  • “Release It!” - operational reliability

10.2 Tools & Documentation

  • OpenTelemetry docs: https://opentelemetry.io/
  • Previous Project: Evaluation Harness & Red Team (P09)

11. Self-Assessment Checklist

11.1 Understanding

  • I can explain system integration trade-offs

11.2 Implementation

  • System passes evaluation harness

11.3 Growth

  • I can define a production readiness checklist

12. Submission / Completion Criteria

Minimum Viable Completion:

  • Orchestrator, memory, and safety integrated

Full Completion:

  • Observability and evaluation harness in place

Excellence (Going Above & Beyond):

  • Distributed execution and scaling added

This guide was generated from LEARN_COMPLEX_MULTI_AGENT_SYSTEMS_DEEP_DIVE.md. For the complete learning path, see the README.