Agent Architecture Design Patterns
As large language models continue to improve, AI products are moving beyond one-shot question answering. They now describe agents that can plan, use tools, inspect results, and revise their approach.
That shift makes architecture more important than prompting alone. A strong agent design turns model output into a controlled loop.
The Basic Agent Loop
A standard agent usually contains four parts:
- Perception: The system gathers user input, environmental signals, or multimodal data and converts it into context the model can use.
- Reasoning: The model or a reasoning layer breaks goals into steps, evaluates options, and selects a course of action.
- Action: The agent calls tools, queries systems, runs code, or interacts with external services.
- Memory: The agent stores short-term conversation context and longer-term knowledge or experience.
The loop may run once for simple tasks or many times for complex goals.
Classic Agent Design Patterns
Reactive Pattern
The reactive pattern is the most basic form. It responds to each input with an output and does not maintain deep planning state.
- Best for: simple Q&A, text transformation, single-step tool use.
- Trade-offs: fast and cheap, but weak on multi-step reasoning.
Reflection and Iteration
This pattern adds a feedback cycle. The agent generates, evaluates, and corrects.
- Best for: code generation, debugging, writing, math.
- Trade-offs: higher success rate, more tokens, more latency.
Planning and Decomposition
The agent splits a large goal into subtasks and executes them in order or in parallel.
- Best for: research, data analysis, project workflows.
- Trade-offs: handles larger tasks, adds orchestration complexity.
Multi-Agent Collaboration
Multiple specialized agents work together, each with its own role and tools.
- Best for: full software lifecycles, complex business workflows.
- Trade-offs: stronger division of labor, higher coordination cost.
Comparing the Patterns
| Pattern | Complexity | Failure Handling | Typical Latency | Common Uses |
|---|---|---|---|---|
| Reactive | Low | Low | Short | Support, retrieval |
| Reflection | Medium | Medium | Medium | Code, writing |
| Planning | Medium-High | Medium-High | Long | Workflows, reports |
| Multi-Agent | High | High | Long | Enterprise automation |
Building Reliable Agents
A practical architecture often combines patterns. A planner may use reflection before execution. A multi-agent system may use reactive components for simple tasks.
Key concerns include tool safety, state management, evaluation, and human oversight.
The Road Ahead
Agent architecture will keep evolving toward greater autonomy, lower latency, and stronger human-agent collaboration. The winning designs will balance model flexibility with system control.