Agent Capability Patterns

Capability patterns are the architectural building blocks that make AI agents powerful. While a basic LLM generates text in a single pass, an agentic system combines multiple patterns — reflection, tool use, planning, collaboration, and memory — to tackle complex, multi-step tasks autonomously.

These patterns were popularized by Andrew Ng in his 2024 writing on agentic design patterns (The Batch, DeepLearning.AI), building on research from Google, Stanford, Princeton, and others. The patterns are platform-agnostic — they apply whether you’re building with Claude, ChatGPT, Gemini, or any other LLM.

Pattern Catalog

Capability Patterns

These five patterns define what agents can do.

Pattern	What It Does	Key Benefit
Reflection	Agent reviews and critiques its own output, then improves it	Higher-quality results through self-correction
Tool Use	Agent calls external tools, APIs, and data sources	Extends capabilities beyond text generation
Planning	Agent breaks complex goals into a sequence of steps	Handles multi-step tasks that require strategy
Multi-Agent Collaboration	Multiple specialized agents work together on a task	Tackles problems too complex for a single agent
Memory	Agent stores and retrieves information across interactions	Learns from experience and maintains context

Safety & Control

These two patterns define how agents stay safe and aligned.

Pattern	What It Does	Key Benefit
Guardrails	Automated rules that constrain agent behavior	Prevents harmful or off-topic outputs without human intervention
Human-in-the-Loop	Human checkpoints at key decision points	Keeps humans in control of high-stakes actions

How Patterns Work Together

Consider a customer exchange request — a customer wants to return a product and exchange it for a different item. A capable agent doesn’t just use one pattern; it combines several:

1. Planning — The agent breaks the exchange into steps: verify the order, check return eligibility, find the replacement item, process the return, place the new order.
2. Tool Use — At each step, the agent calls the order management API, inventory system, and payment processor.
3. Reflection — Before confirming the exchange, the agent reviews its work: “Did I verify the return window? Is the replacement item in stock? Are the prices correct?”
4. Guardrails — Automated rules prevent the agent from issuing refunds above a threshold or processing exchanges for final-sale items.
5. Human-in-the-Loop — If the exchange involves an exception (expired return window, high-value item), the agent escalates to a human agent for approval.
6. Memory — The agent remembers this customer’s preferences and past interactions, personalizing future service.

No single pattern makes this workflow possible. Their combination is what turns a basic chatbot into a capable agent.

Looking for multi-agent coordination?

Capability patterns describe what individual agents can do. For how multiple agents coordinate — sequential pipelines, parallel fan-out, hierarchical delegation, and more — see Orchestration Patterns.

Where to Start

Understanding agent concepts? Start with Reflection — it’s the simplest pattern to grasp and demonstrates the core idea of iterative improvement.

Building your first agent? Tool Use is the most immediately practical pattern — it’s how agents interact with the real world.

Designing a production system? Read Guardrails and Human-in-the-Loop first — safety and control should be designed in from the start, not bolted on later.

Related

• Agents — The Agents building block overview
• Agents Resources — Recommended reading on agents
• Building Effective Agents — Anthropic’s guide to agent architecture
• Prompt Engineering — Techniques for the prompts that drive agent behavior
• Automation Use Cases — Real-world applications of agentic systems