AI Agent Design Patterns

What Makes an Agent

A chatbot answers questions. An agent takes action. The difference is autonomy: an agent receives a goal, plans how to achieve it, uses tools to interact with the world, observes the results, and adapts its approach. The core loop is deceptively simple:

Plan → Act → Observe → Reflect → (repeat until goal is met)

But the design patterns within this loop determine whether your agent is reliable or chaotic.

The ReAct Pattern

ReAct (Reason + Act) is the most widely-used agent pattern. The model alternates between reasoning steps (thinking about what to do) and action steps (calling tools). Each observation feeds back into the next reasoning step.

Thought: I need to find the user's recent deployments
Action: call list-deployments tool with user_id="abc123"
Observation: Found 3 deployments, latest is deploy-789
Thought: Now I should check the status of the latest deployment
Action: call get-deployment-status tool with id="deploy-789"
Observation: Status is "failed" with error "OOM killed"
Thought: The deployment failed due to memory. I should suggest increasing limits.

ReAct works because it forces structured reasoning before every action. The model doesn't just blindly chain tool calls — it explains its logic at each step.

Tool-Augmented Generation

Not every task needs a full agent loop. Tool-augmented generation is the simpler pattern where the model makes one or two tool calls to enhance its response. Think of a coding assistant that runs a search before answering, or a support bot that looks up a customer record.

Use this pattern when: the task is well-defined, requires minimal planning, and the tool calls are predictable. Use full agents when: the task is open-ended, requires multiple steps, or the path to the goal is uncertain.

Memory Systems

Effective agents need memory beyond the current conversation:

Short-term memory is the conversation context — what's been said and done in this session. It's handled naturally by the LLM's context window.

Long-term memory persists across sessions. This could be a vector database of past interactions, a knowledge graph, or structured logs. The agent retrieves relevant memories before acting.

Episodic memory records specific past experiences: "Last time I deployed to staging, the health check took 30 seconds." This helps agents avoid repeating mistakes and learn team-specific patterns.

Error Recovery and Guardrails

Production agents must handle failure gracefully. Key strategies:

Retry with variation: If a tool call fails, don't just retry — adjust the approach. Change parameters, try an alternative tool, or gather more information first.

Escalation: Know when to stop. If an agent can't resolve an issue after N attempts, it should escalate to a human rather than looping indefinitely.

Guardrails: Define hard constraints the agent must never violate. File deletion requires confirmation. Production deployments need approval. Sensitive data is never logged. These aren't suggestions — they're enforced boundaries.

Structured output validation: Validate tool outputs before using them. A malformed API response shouldn't crash the agent loop.

When Simple Beats Complex

A common mistake is over-engineering. If your use case is "search docs and answer questions," you don't need a multi-step agent with memory systems. Start with simple tool-augmented generation and add complexity only when you hit real limitations.

Arandu demonstrates the full spectrum — it operates as an autonomous agent with terminal, browser, and editor access, handling complex tasks that genuinely require planning, multi-step execution, and adaptive reasoning. But even Arandu defaults to the simplest approach that works for each specific subtask.

The best agent is the simplest one that reliably achieves the goal.