AI Agent Architecture Patterns: A Practical Guide

What Are AI Agent Architecture Patterns?

AI agent architecture patterns are reusable design structures that define how autonomous agents sense their environment, make decisions, and take actions. They're blueprints for building AI systems that work independently toward goals. Chatbots respond to queries. Agents operate in loops. They observe their environment, decide what to do, take action, and observe the results. The architecture pattern you choose determines how these loops work and how tasks get broken down. Three core models define agent architectures today:

• Reactive agents respond directly to environmental stimuli without maintaining internal state
• Deliberative agents maintain world models and plan sequences of actions
• Hybrid agents combine reactive responses with deliberative planning

The pattern you select depends on task complexity, reliability requirements, and how much autonomy you want to grant. A customer service bot might use simple reactive patterns. A research assistant needs deliberative planning.

The Four Core Architecture Patterns

Google's research team identified three foundational execution patterns that underpin all agent architectures: sequential, loop, and parallel. From these foundations, four practical patterns have emerged that power most production agent systems.

Each pattern makes different tradeoffs between capability and predictability. ReAct agents are easy to debug but limited in scope. Multi-agent systems can handle harder problems but introduce coordination overhead.

ReAct Pattern: Reasoning Plus Acting

The ReAct pattern (Reasoning and Acting) interleaves thought and action in a single loop. The agent thinks about what to do, takes an action, observes the result, thinks again, and repeats until the task completes. Research shows ReAct improves agent accuracy by approximately 30% compared to pure action-based approaches. The explicit reasoning traces make it easier to understand why an agent made specific decisions.

How ReAct Works:

Thought: The agent reasons about the current state and what action to take 2.

Action: The agent executes a single tool or API call 3.

Observation: The agent receives and processes the result 4.

Repeat: The loop continues until the task is complete

When to Use ReAct:

• Question-answering with tool access
• Simple research tasks
• Single-domain problems
• Situations where explainability matters

Example ReAct Trace:

Task: Find the latest quarterly revenue for Acme Corp

Thought: I need to search for Acme Corp's financial reports
Action: search("Acme Corp Q4 2025 earnings report")
Observation: Found press release dated January 15, 2026

Thought: I found a relevant document. Let me extract the revenue figure. Action: read_document(url)
Observation: "Q4 revenue reached $2.3 billion, up 12% year-over-year"

Thought: I have the answer. Action: respond("Acme Corp's latest quarterly revenue was $2.3 billion")

ReAct works well when tasks are simple enough for step-by-step reasoning but still require tool use. It struggles with tasks that need parallel execution or long-horizon planning.

Plan-and-Execute Pattern: Breaking Down Complex Tasks

The Plan-and-Execute pattern separates planning from execution into two distinct phases. First, a planner agent creates a step-by-step plan. Then, an executor agent works through each step sequentially. This separation has real benefits:

• Better decomposition: Complex tasks get broken into manageable steps before execution begins
• Easier debugging: You can inspect and modify plans before execution
• Checkpoint support: Progress can be saved and resumed at any step

Architecture Overview:

┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│   Planner   │────▶│    Plan     │────▶│  Executor   │
│   (LLM)     │     │   (JSON)    │     │   (Loop)    │
└─────────────┘     └─────────────┘     └─────────────┘
                          │
                          ▼
                   ┌─────────────┐
                   │   Storage   │
                   │  (State)    │
                   └─────────────┘

When to Use Plan-and-Execute:

• Multi-step research and analysis
• Content creation pipelines
• Data processing workflows
• Tasks where you want human review of the plan before execution

Persistent Storage Matters:

Plan-and-Execute agents need persistent storage to track progress, save intermediate results, and enable recovery from failures. Without storage, a failed step means starting over from scratch. Agents using Fast.io's storage API can save plans as JSON files, checkpoint progress after each step, and maintain audit logs of all actions taken. This makes debugging production agents far simpler than ephemeral approaches.

Multi-Agent Pattern: Specialized Teams

The Multi-Agent pattern assigns different specialized agents to different parts of a problem. Instead of one agent doing everything, multiple agents collaborate, each bringing specific expertise. Research indicates multi-agent systems can handle 5x more complex tasks than single-agent approaches. Specialization lets each agent focus on what it does best.

Common Multi-Agent Architectures:

Coordinator/Dispatcher: One agent receives requests and routes them to specialized workers. ``` ┌─────────────┐ │ Coordinator │ └──────┬──────┘ │ ┌──────┼──────┬──────────┐ ▼ ▼ ▼ ▼ ┌────┐ ┌────┐ ┌────┐ ┌────────┐ │Code│ │Data│ │Docs│ │Research│ └────┘ └────┘ └────┘ └────────┘

**Generator and Critic**: One agent creates content while another validates and provides feedback.

**Sequential Pipeline**: Agents pass output to the next in a chain, like an assembly line.

**When to Use Multi-Agent:**

- Tasks spanning multiple domains (code + data + documentation)
- Quality-critical applications where review is essential
- High-volume processing where parallelization helps
- Complex workflows with distinct phases

**Coordination Challenges:**

Multi-agent systems introduce communication overhead. Agents need shared context to collaborate well. This is where agent memory architecture matters most. Shared workspaces solve the coordination problem. When agents can read and write to common file locations, they share context naturally. Agent A writes its research findings to a workspace. Agent B reads those files as input for its analysis. No complex message-passing protocols required. 

Tool-Use Pattern: Connecting to External Systems

The Tool-Use pattern gives agents access to external APIs, databases, and services. Agents can fetch real-time data, execute code, and interact with other systems instead of relying only on the LLM's knowledge.

Common Tool Categories:

• Search: Web search, vector databases, file search
• Data: SQL queries, API calls, file operations
• Compute: Code execution, calculations, transformations
• Communication: Email, messaging, notifications

The Model Context Protocol (MCP):

MCP standardizes how agents connect to external tools. Agents speak a common protocol instead of building custom integrations for each tool. This reduces integration work and lets different agent implementations share tools. Fast.io provides an official MCP server for file operations. Agents using MCP-compatible frameworks like Claude can read, write, and share files without custom API code. The MCP server handles authentication, permissions, and file management.

Tool Selection Strategies:

Static tool sets: Agent always has access to the same tools 2.

Dynamic tool loading: Tools selected based on task requirements 3.

Hierarchical tools: Meta-tools that invoke other tools

Storage as a Foundational Tool:

Every production agent needs persistent storage. Temporary file systems work for demos, but real applications require:

• Persistent file storage that survives restarts
• Shared access for multi-agent collaboration
• Permission controls for security
• Audit logs for debugging and compliance

Agents can sign up for their own Fast.io accounts, create workspaces, and manage files programmatically. The free tier provides 5,000 credits monthly, enough for development and small production workloads.

Agent Memory Architecture

Memory determines what an agent knows and remembers across interactions. Without memory, every conversation starts from scratch. With good memory architecture, agents build context over time and get better at their jobs.

Three Types of Agent Memory:

Working Memory:

Working memory holds the current task context, including the original request, intermediate results, and tool outputs. This is typically in-memory and lost when the agent process ends.

Short-Term Memory:

Short-term memory persists across a conversation or session but resets between sessions. Chat history is the most common example. Session storage or caches work well here.

Long-Term Memory:

Long-term memory persists indefinitely. This includes learned preferences, accumulated knowledge, and historical records. Long-term memory requires persistent storage.

Implementing Long-Term Memory:

Agents need a persistent storage layer that:

• Maintains files and data between sessions
• Supports organization (folders, workspaces)
• Enables search and retrieval
• Handles large files and datasets

Cloud storage designed for agents solves these requirements. Agents use storage APIs to save and retrieve information instead of managing databases or file systems themselves. Workspaces keep projects organized. Semantic search helps find relevant past work.

Building Production Agent Systems

Moving from prototype to production means handling failures gracefully, seeing what your agents are doing, and scaling up when needed. The architecture patterns provide the foundation, but production systems need more infrastructure.

State Management:

Production agents must recover from failures. This means:

• Checkpointing progress at each step
• Storing intermediate results persistently
• Enabling resume from the last successful checkpoint
• Maintaining audit logs for debugging

Without persistent state, a failed API call or timeout means losing all progress. Agents should save their work continuously, not just at task completion.

Observability:

You need visibility into what agents are doing:

• Logging: Record every decision and action
• Tracing: Follow request flows across agents
• Metrics: Track success rates, latencies, costs
• Alerts: Notify on failures or anomalies

Good audit logs show exactly what each agent did and when. When something goes wrong, you can trace back through the logs to understand the failure.

Human-in-the-Loop:

Many production systems include human checkpoints:

• Plan review before execution
• Approval for high-impact actions
• Escalation for uncertain situations
• Quality review of outputs

Shared workspaces make human-agent collaboration possible. Humans and agents work in the same file system, reviewing each other's work and handing off tasks naturally.

Cost Management:

LLM API calls add up quickly. Production systems need:

• Token usage tracking
• Caching for repeated queries
• Model selection based on task complexity
• Budget limits and alerts

Choosing the Right Pattern

Pattern selection depends on your specific requirements. Here's a decision framework:

Start with ReAct if:

• Your task is straightforward question-answering with tools
• You need clear reasoning traces for debugging
• You're building a prototype or MVP

Move to Plan-and-Execute if:

• Tasks involve multiple distinct steps
• You want human review of plans before execution
• Failure recovery is important

Consider Multi-Agent if:

• Tasks span multiple specialized domains
• You need parallel processing for throughput
• Quality validation requires a separate reviewer

Use Tool-Use throughout:

• Every pattern benefits from tool access
• MCP provides standardized integration
• Storage should be a core tool for any production agent

Hybrid Approaches:

Real systems often combine patterns. A multi-agent system might use ReAct within each specialized agent. A Plan-and-Execute system might spawn sub-agents for complex steps. Match your architecture complexity to your problem complexity. Simpler patterns are easier to debug and maintain. Add complexity only when simpler approaches fail.