Building Effective AI Agents

Successful AI agents are built using simple, composable patterns instead of complex frameworks.

Agents can be categorized into workflows and agents, where workflows are predictable systems with predefined paths, and agents are dynamic and adapt to tasks.

Agentic systems trade latency and cost for improved task performance, so they should be used when such tradeoffs are justifiable.

Frameworks like LangGraph and Amazon Bedrock's AI Agent can be used to facilitate agent creation, but understanding and testing the underlying code is crucial.

Several workflows like prompt chaining, routing, parallelization, orchestrator-workers, and evaluator-optimizer offer different methods to enhance LLM performance based on task needs.

Agents excel in open-ended tasks requiring decision-making and adaptability, but require proper testing to handle errors and maintain control.

The key to effective agentic systems is to maintain simplicity, transparency, and a well-documented interface for agents.

Practical use cases include customer support and coding agents, which benefit from enhanced task performance and effective feedback integration.