Skill Guide

Agent design patterns (ReAct, plan-and-execute, reflection, tree-of-thought)

Agent design patterns are reusable architectural templates (like ReAct, plan-and-execute, reflection, tree-of-thought) that define how an AI agent reasons, plans, acts, and learns to solve complex tasks.

These patterns enable the development of reliable, auditable, and high-performing autonomous agents, directly impacting business outcomes by automating complex, multi-step workflows that were previously infeasible, leading to significant gains in efficiency and innovation. Mastering them allows engineers to build robust AI systems that are predictable and scalable, moving beyond brittle, single-prompt solutions.

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How to Learn Agent design patterns (ReAct, plan-and-execute, reflection, tree-of-thought)

Focus on: 1) Understanding the core cognitive loop of an agent (Perception -> Reasoning -> Action). 2) Memorizing the canonical execution flow of each pattern (e.g., ReAct's interleaved Thought/Action/Observation). 3) Implementing a simple ReAct agent for a single-tool task (e.g., a Wikipedia Q&A agent) using LangChain or LlamaIndex.

Focus on: 1) Combining patterns (e.g., using a tree-of-thought for initial planning within a plan-and-execute framework). 2) Implementing robust error handling and fallback mechanisms within each pattern. 3) Analyzing agent logs to debug reasoning failures. A common mistake is forcing a complex pattern like ToT for simple tasks, incurring unnecessary cost and latency.

Focus on: 1) Designing hybrid, self-optimizing agent architectures where patterns are dynamically selected based on task complexity. 2) Aligning agent architecture with system-level non-functional requirements (cost, latency, accuracy). 3) Creating benchmarks and evaluation frameworks to quantitatively compare pattern performance. Mastery involves mentoring teams on pattern trade-offs and building organizational agent design standards.

Practice Projects

Beginner

Project

Build a ReAct Research Assistant

Scenario

Create an agent that can answer questions by iteratively searching the web (using a search API like SerpAPI), reading snippets, and synthesizing an answer.

How to Execute

1. Set up a LangChain Agent with a `Search` tool. 2. Use the `AgentType.REACT_DOCSTORE` or similar ReAct prompt. 3. Give it a series of questions (e.g., 'What is the population of the capital of France?'). 4. Log and analyze the full Thought/Action/Observation chain to understand the reasoning flow.

Intermediate

Project

Implement a Plan-and-Execute System for Market Analysis

Scenario

Build an agent that, given a high-level goal like 'Analyze the competitive landscape for electric vehicles in Europe,' will first create a multi-step plan, then execute each step using specialized tools (web search, data analysis, file writing).

How to Execute

1. Use a framework like LangGraph or AutoGPT's planning module. 2. Define a planner LLM that decomposes the goal into steps (e.g., Step1: List top 5 EV manufacturers. Step2: Collect sales data for each. Step3: Analyze sentiment from recent news). 3. Define an executor agent with tools for each sub-task. 4. Implement a re-planning step if an execution fails.

Advanced

Project

Design a Tree-of-Thought Debugging Agent for Complex Codebases

Scenario

Create an agent that, given a complex bug report, explores multiple potential root causes in parallel (like a tree), evaluates the likelihood of each path (e.g., by writing and running unit tests), and backtracks to explore alternative hypotheses when a branch is exhausted.

How to Execute

1. Structure the agent to generate multiple initial hypotheses (branches) for the bug. 2. For each branch, assign a 'score' or 'confidence' based on outcomes of diagnostic actions (e.g., test results, log analysis). 3. Implement a search algorithm (e.g., beam search) to expand the most promising branches while pruning low-scoring ones. 4. Integrate with a CI/CD pipeline to run tests in isolated environments. The output is a ranked list of root causes with supporting evidence.

Tools & Frameworks

Software & Platforms

LangChain/LangGraphLlamaIndexAutoGPT/CrewAI

LangChain/LangGraph provide the core primitives for defining chains, agents, and state machines, making them essential for implementing any pattern. LlamaIndex is specialized for retrieval-augmented agents. AutoGPT/CrewAI offer higher-level, opinionated implementations of multi-agent plan-and-execute patterns.

Conceptual Frameworks

Chain-of-Thought (CoT)Self-ConsistencyReflection/Self-Refine

These are not libraries but prompting/technique frameworks. CoT is the foundation for ReAct's 'Thought'. Self-Consistency is a voting mechanism used within tree-of-thought to select the best outcome from multiple reasoning paths. Reflection is the core loop for self-correcting agents.

Evaluation & Observability

LangSmithWeights & BiasesCustom Logging

Critical for debugging and improving agent patterns. LangSmith provides tracing for LangChain agents, showing every step. W&B can log metrics for different pattern runs. Custom logging is necessary for production systems to audit agent reasoning and tool usage.

Interview Questions

Answer Strategy

The interviewer is testing pattern selection based on task decomposition. Strategy: Choose Plan-and-Execute for its clear separation of planning and execution. Sample answer: 'I'd use a Plan-and-Execute pattern. The high-level goal is clear, but the exact API calls and data transformations are variable. A Planner LLM would first generate a step-by-step plan: 1) Identify required data sources, 2) Call APIs in parallel, 3) Apply cleaning functions, 4) Generate visualizations, 5) Compile report. An Executor agent, equipped with specific tools for each step, would then carry out the plan. This is superior to a monolithic ReAct agent because it provides a clear audit trail, allows for re-planning if an API fails, and separates strategic planning from tactical execution.'

Answer Strategy

Tests understanding of pattern limitations. Core competency: Identifying failure modes and matching patterns to problems. Sample answer: 'A pure ReAct agent struggles with tasks requiring parallel exploration or exhaustive search. For example, solving a complex logic puzzle or exploring multiple solution paths in a game. I'd switch to a Tree-of-Thought pattern. Instead of a single linear chain, the agent would generate multiple initial thoughts (branches), evaluate their promise, and explore the most promising ones. This allows for backtracking and exploring alternatives, which is essential for problems with high ambiguity or combinatorial complexity, though it comes at a higher computational cost.'