AI Agent Architecture Patterns: ReAct, Plan-Execute, and Multi-Agent Debate

The Agent Architecture Problem

You've built an AI agent. It works in your notebook. You deploy it to production. Then:

• It enters an infinite reasoning loop and burns $400 in tokens overnight
• A tool response contains "ignore previous instructions" and your agent leaks customer data
• Three agents calling the same LLM provider simultaneously all get rate-limited
• The agent's context window fills up and it starts hallucinating

These aren't hypothetical. They're the actual failure modes that AI teams discover in production — after it's too late.

You can simulate every one of these before deploying. Here's how each pattern works, what breaks, and how to test it.

The 8 Patterns

1. ReAct Loop (Think → Act → Observe)

The most common pattern. The agent reasons about what to do, takes an action (usually a tool call), observes the result, then reasons again.

User Query → Think → Tool Call → Observe → Think → Tool Call → Observe → Answer

What breaks: Infinite loops. The agent keeps thinking and calling tools without making progress. Each iteration costs tokens. Without a loop detector, it runs until the budget is exhausted.

Simulation setup: Add a Reasoning Tracer + Token Budget Controller. Run the simulation and watch what happens when the agent hits max iterations. Does it gracefully degrade or burn tokens?

2. Plan-and-Execute

A planner agent creates a multi-step plan, then worker agents execute each step in parallel.

Task → Planner → [Worker A, Worker B, Worker C] → Aggregator → Result

What breaks: Worker divergence. Worker A returns a result that contradicts Worker B. The aggregator has no way to resolve the conflict. Or the planner creates an overly ambitious plan with 20 steps, each costing 2000 tokens.

Simulation setup: Watch token cost accumulate across workers. Test with a task that's too complex — does the planner cap its plan length?

3. RAG Pipeline

Query → Embed → Retrieve from vector store → Inject into prompt → Generate → Validate

Query → Embedding → Vector DB → Context Injection → LLM → Output Validator → Response

What breaks: Memory poisoning. Wrong embeddings in the vector store return irrelevant context. The LLM confidently generates wrong answers because it trusts the retrieved context. Also: context window overflow when too many chunks are retrieved.

Simulation setup: Add a Retrieval Validator between the vector store and the LLM. Add a Context Compressor to summarize large retrievals. Test with queries that have no good match in the store.

4. Multi-Agent Debate

Three agents argue different perspectives. A judge agent picks the best answer.

Moderator → Agent A → Agent B → Agent C → Judge → Final Answer

What breaks: Cost explosion. Debates can go on for 5+ rounds. Each agent uses 1500 tokens per round. 3 agents × 5 rounds × 1500 tokens = 22,500 tokens per query. At GPT-4o pricing, that's $0.056 per query — 20x more than a single-shot call.

Simulation setup: Add a Token Budget Controller with a per-task cap. Watch the cost counter in real-time. Compare debate quality (confidence score) vs single-shot.

5. Supervisor + Workers (CrewAI Pattern)

A supervisor agent dispatches tasks to specialist workers, reviews their output, and iterates until satisfied.

Supervisor → Research Agent → Analysis Agent → Writing Agent → Supervisor Review → Output

What breaks: Supervisor bottleneck. All workers report to one supervisor. If the supervisor uses a slow model (like o3), every task waits for the supervisor's review. Throughput collapses.

Simulation setup: Test with different models for the supervisor (o3 vs Sonnet vs Haiku). Watch throughput change. Is the quality improvement from o3 worth the 5x latency?

6. Tool Chain Pipeline

Agent calls Tool A, takes the result to Tool B, then Tool C, then Tool D. Sequential dependency.

Agent → Search API → Extract → Transform → Load → Agent Reviews

What breaks: Cascading timeout. Tool C takes 30 seconds instead of the usual 200ms. Tool D never gets called. The agent retries Tool C three times. Total: 90 seconds of waiting, then failure.

Simulation setup: Add circuit breakers between tools. Configure timeouts. Test: what happens when one tool in the chain is down? Does the agent skip it or hang?

7. AI Safety Stack

Every input and output goes through safety layers: input guardrails, output validation, PII scrubbing, prompt version management.

Input → Input Guardrails → LLM → Output Guardrails → PII Scrubber → Output Validator → Response

What breaks: Prompt injection via tool responses. The safety stack checks user input, but a tool returns malicious instructions embedded in its response. The LLM follows them because they came from a "trusted" source.

Simulation setup: This is the hardest scenario to test. Add an Output Validator between every tool response and the LLM. Add guardrails on BOTH input and output paths.

8. Model A/B Testing

Split traffic between two models. Compare quality, latency, and cost in production.

Traffic Splitter → Model A (GPT-4.1) / Model B (Sonnet 5) → Evaluation → Winner Selection

What breaks: Model drift. Model B gets updated by the provider. Quality changes subtly. Your evaluation harness doesn't catch it because the metrics look similar — but user satisfaction drops.

Simulation setup: Run the same test prompts through both models. The Evaluation Harness detects regression by comparing confidence scores and output structure. Shadow testing catches drift before it reaches users.

Choosing the Right Pattern

| Use Case | Pattern | Why | |---|---|---| | Simple Q&A with tools | ReAct | Low overhead, easy to debug | | Complex multi-step tasks | Plan-Execute | Parallel workers, fast for batch | | High-accuracy requirements | Debate | Multiple perspectives, higher quality | | Team coordination | Supervisor | Structured workflow, human-like review | | Data processing pipeline | Tool Chain | Sequential, deterministic | | Production safety | Safety Stack | Defense in depth | | Model evaluation | A/B Testing | Data-driven model selection | | Knowledge retrieval | RAG | Grounded, reduces hallucination |

Simulate Before You Deploy

Every pattern above has failure modes that are invisible in development but catastrophic in production. The difference between a $0.001/query agent and a $0.50/query agent is often one missing circuit breaker or token budget.

Try all 8 patterns — each is available as a pre-built template. Load one, run the simulation, inject failures, and see what breaks.