A prompt-in, response-out model is a calculator. An agent is a programmer who knows when to use the calculator. The difference is architecture — the patterns that determine how an AI system reasons, plans, acts, observes, and corrects. This article covers the major agent architectures and when to use each.

ReAct: Reason and Act

The simplest effective agent pattern. The model alternates between reasoning (thinking about what to do) and acting (calling tools or generating output). After each action, it observes the result and reasons about the next step.

Strengths: Simple to implement, works well for straightforward multi-step tasks, easy to debug because reasoning is visible.

Weaknesses: No upfront planning — the agent makes decisions one step at a time. Can get stuck in loops or pursue dead-end strategies. Poor at tasks requiring coordination of many steps.

Use when: Tasks have 2-5 steps, each step has clear success/failure signals, and the problem does not require long-term planning.

Plan-and-Execute

A two-phase pattern. First, a planning model creates a multi-step plan. Then, an execution model carries out each step, reporting results back to the planner. The planner can revise the plan based on execution results.

Strengths: Better at complex tasks requiring coordination. The plan provides a roadmap that prevents aimless exploration. Separation of planning and execution allows using different models for each role.

Weaknesses: Plans can be wrong. Replanning is expensive. The interface between planner and executor must be carefully designed.

Use when: Tasks have 5+ steps, require resource management or sequencing, or benefit from a high-level strategy.

Multi-Agent Systems

Multiple specialized agents collaborate on a task. Each agent has a defined role (researcher, coder, reviewer, coordinator) and they communicate through a shared protocol.

Strengths: Enables specialization. A coding agent can be tuned differently from a research agent. Allows parallel execution. Can handle very complex workflows.

Weaknesses: Communication overhead. Coordination failures. Harder to debug. More expensive (multiple model calls per step).

Use when: The task has genuinely distinct subtasks requiring different capabilities, and the overhead of coordination is justified by the complexity.

Choosing the Right Pattern

Start simple. A ReAct loop handles 80% of agent use cases. Only add planning if you observe the agent making poor strategic decisions. Only add multi-agent coordination if the task genuinely requires multiple specialists.

Common Misconceptions

"More agents means better results"

More agents means more communication overhead and more potential failure points. A single well-prompted agent with good tools often outperforms a poorly coordinated multi-agent system.

Frequently Asked Questions

Can I build agents with local models?

Yes. Models with tool-calling support running through Ollama work well for agent loops. The key is choosing a model with strong instruction-following and tool-use capabilities.

What about safety?

Agent safety is critical. Always implement guardrails: maximum step counts, action whitelists, human-in-the-loop for destructive actions, and output validation. An agent with unrestricted tool access is a security risk.