Chapter 2: AI Agent Architectures and Design Patterns

Building effective AI agents requires understanding underlying architectural patterns that enable autonomous behavior. Architecture determines how agents perceive, reason, and act. Choosing appropriate patterns for your use case is crucial for creating reliable, efficient, and maintainable agents.

The Basic Agent Loop

At its core, every AI agent operates on a fundamental loop: perceive the current state, reason about what action to take, act by executing the chosen action, and evaluate the outcome. This cycle repeats until the agent achieves its goal or determines the goal is unattainable. Understanding this basic loop is essential before exploring sophisticated architectures.

In practice, this loop involves reading inputs from the environment (user messages, API responses, file contents), processing information using the agent's reasoning engine (typically an LLM), selecting and executing actions (calling functions, making API requests, generating responses), and updating the agent's state and memory based on results.

React: Reasoning and Acting

ReAct (Reasoning and Acting) is one of the most influential agent architectures. It interleaves reasoning steps with actions, allowing agents to think through problems systematically while taking actions based on reasoning.

The ReAct pattern follows a structured approach. The agent receives a task or question, then enters a loop where it reasons about the current situation using "Thought" steps (the LLM verbally works through logic), decides what action to take using "Action" steps (calling tools, APIs, or functions), receives observations from executed actions using "Observation" steps, and continues reasoning based on new information.

For example, if asked "What's the weather in the capital of France?", a ReAct agent might think: "I need to know France's capital first," then action: "Search for 'capital of France'," observe: "The capital of France is Paris," think: "Now I need Paris weather," action: "Get weather for Paris," observe: "Current weather in Paris is 15°C, partly cloudy," and finally respond: "The weather in Paris, France's capital, is 15°C and partly cloudy."

This explicit reasoning trace makes agents more interpretable and helps prevent errors by forcing systematic thinking.

Plan-and-Execute Architecture

Plan-and-Execute agents separate planning from execution, first creating complete action plans then executing them step-by-step. This architecture works well for complex, multi-step tasks where upfront planning improves efficiency.

The process begins with task decomposition, where the agent breaks complex goals into smaller sub-tasks. It then performs dependency analysis to understand task ordering requirements. Next comes plan creation, generating a detailed sequence of actions. The agent then moves to execution, carrying out each step in order, and finally does validation, verifying each step succeeded before continuing.

For instance, given the goal "Research competitors and create a comparison report," a Plan-and-Execute agent might create this plan: identify main competitors in the market, gather information about each competitor's products, collect pricing data for each offering, analyze strengths and weaknesses, generate comparative analysis, and format findings into a structured report. Only after creating this complete plan would execution begin.

The advantage is efficiency—thorough planning reduces redundant actions. The disadvantage is rigidity—if early steps fail or circumstances change, the entire plan may need revision.

Reflection and Self-Critique

Reflection patterns allow agents to evaluate their own outputs and refine them through self-critique. This iterative refinement often produces higher-quality results than single-pass approaches.

The reflection cycle works as follows: the agent generates an initial solution or output, then critiques its own work identifying weaknesses, errors, or improvements. It revises the output based on self-critique, and repeats until the output meets quality standards or iteration limits are reached.

This pattern is particularly effective for creative tasks, complex reasoning problems, and quality-sensitive outputs like code generation or writing. For example, a coding agent might write a function, analyze it for bugs or inefficiencies, refactor the code, test it mentally or by execution, and iterate until satisfied.

Implementing reflection requires careful prompt engineering—the agent needs clear criteria for evaluation and must be prevented from infinite loops of unnecessary refinement.

Multi-Agent Systems

Rather than single powerful agents, multi-agent systems employ multiple specialized agents collaborating to solve problems. Each agent has specific expertise and responsibilities, and agents coordinate to achieve shared goals.

Common multi-agent patterns include hierarchical systems where a manager agent delegates tasks to specialized worker agents, collaborative systems where peer agents work together negotiating and coordinating, and competitive systems where agents propose different solutions and the best is selected.

For example, a content creation system might include a researcher agent gathering relevant information, a writer agent drafting content, an editor agent reviewing and improving writing, a fact-checker agent verifying claims, and an SEO agent optimizing for search engines. Each agent excels at its specialty, and together they produce superior results to a single generalist agent.

Frameworks like CrewAI and AutoGen facilitate building multi-agent systems by handling communication, coordination, and conflict resolution between agents.

Memory Architectures

Effective agents require sophisticated memory systems going beyond simple conversation history. Memory architecture determines what information agents retain and how they access it.

Short-term memory holds immediate context—the current conversation, recent actions, and working variables. This memory is typically implemented as a rolling window of recent interactions or a summarized context.

Long-term memory stores knowledge across sessions—learned facts, user preferences, past successful strategies, and domain knowledge. Vector databases excel at long-term memory by enabling semantic search over large information stores.

Episodic memory records specific past experiences and their outcomes. Agents consult episodic memory to avoid repeating mistakes and replicate past successes.

Semantic memory contains general knowledge and learned concepts not tied to specific experiences. This is often provided by the base LLM's training or through retrieval-augmented generation (RAG) systems.

Tool Use and Function Calling

Modern agents extend their capabilities by using external tools—APIs, databases, calculators, code interpreters, and other services. Effective tool use requires careful architecture.

Tool registration makes tools available to agents by defining what each tool does, what parameters it requires, and when to use it. The agent reasoning process includes considering which tools (if any) to use for current needs. Tool execution involves calling the selected tool with appropriate parameters. Result integration means interpreting tool outputs and incorporating them into ongoing reasoning.

Function calling (offered by models like GPT-4 and Claude) provides structured interfaces for tool use. Agents receive tool descriptions, and the model outputs structured function calls rather than just text. This improves reliability compared to parsing tool usage from unstructured text.

Error Handling and Recovery

Robust agents must handle failures gracefully. Error handling architecture determines how agents respond to problems.

Common strategies include retry logic with exponential backoff for transient failures, fallback strategies trying alternative approaches when primary methods fail, and validation and verification checking results before proceeding. Agents need error escalation, knowing when to request human assistance, and checkpointing to save progress, enabling recovery from crashes.

For example, if an API call fails, a well-architected agent might retry with exponential delays, try an alternative API if available, simplify the request if it might be too complex, and ask the user for help if all automatic recovery attempts fail.

Observability and Monitoring

Production agents require observability—the ability to understand what agents are doing and why. This includes logging all reasoning steps, actions, and observations, tracing request flows through multi-step processes, metrics tracking like success rates, response times, and costs, and debugging tools for investigating problems.

Frameworks like LangSmith and Helicone provide observability tools specifically designed for AI agents, making it easier to monitor, debug, and optimize agent behavior.

Choosing the Right Architecture

No single architecture fits all use cases. Simple tasks may need only basic ReAct loops, while complex projects benefit from multi-agent systems with sophisticated memory. Consider these factors: task complexity (simple vs. multi-step workflows), reliability requirements (experimental vs. production systems), interpretability needs (black Box vs. transparent reasoning), and resource constraints (API costs, latency, computing power).

Start simple—basic ReAct agents handle many use cases effectively. Add complexity only when simpler approaches prove insufficient. Over-engineering creates maintenance burdens and debugging challenges without proportional benefits.

Understanding these architectural patterns provides a foundation for building effective agents. The next chapters will cover implementing these patterns using popular frameworks and tools, transforming theoretical knowledge into practical skills.