Agentic Design Patterns

Learn to decompose complex tasks into a series of smaller, manageable LLM calls where the output of one step informs the next.

Implement classification-based routers to direct inputs to specialized prompts or tools based on intent.

Execute multiple LLM calls simultaneously for independent sub-tasks and aggregate the results to reduce latency.

Standardize how models interact with external APIs and databases using structured schema definition.

Utilize MCP to provide agents with a standardized interface for accessing data and tools across different platforms.

Implement Chain-of-Thought and ReAct (Reason + Act) loops to enable agents to 'think' before executing actions.

Build Retrieval-Augmented Generation systems to ground agent responses in external, authoritative knowledge bases.

Learn techniques to optimize token usage and context window management to balance performance and cost.

Develop short-term (buffer) and long-term (vector-based) memory systems to provide agents with persistent context.

Implement a design pattern where a 'critic' agent reviews and improves the output of a 'generator' agent.

Build agents capable of breaking high-level goals into a sequence of actionable steps using hierarchical planners.

Establish mechanisms for agents to define sub-goals and monitor progress toward a final objective.

Enable agents to re-order their task queue based on changing constraints or new information discovered during execution.

Explore patterns like 'Manager-Worker' or 'Joint Workspace' to coordinate multiple specialized agents.

Define protocols for how agents request help from one another and share state information effectively.

Create feedback loops where agents improve their performance over time based on successful outcomes or user feedback.

Implement input/output filtering to prevent hallucinations, PII leaks, and prompt injections.

Build robust agents that can handle API timeouts, invalid tool outputs, and logical deadlocks without crashing.

Integrate checkpoints where humans review, approve, or modify agent decisions before final execution.

Set up observability tools to trace agent reasoning steps and evaluate performance using LLM-as-a-judge metrics.