How to Create AI Agents

Definitions, core capabilities, the evolution of foundational models, and the difference between simple prompts and autonomous agents.

Deconstruction of core building blocks: reasoning engines, memory stores, tool interfaces, and execution controllers.

How to select the right base model, define strict function schemas, and connect agents to external databases and APIs.

Single-agent vs multi-agent topologies, subagent delegation, reflection loops, and sequential execution paths.

Short-term working memory, semantic vector retrieval (RAG), persistent session context, and token budget optimization.

Handling structured JSON outputs, validating execution parameters, retrying transient API errors, and sandboxed code execution.

Concrete walkthroughs of research synthesis agents, automated document processors, and workflow orchestrators.

Evaluation benchmarks, logging agent trajectories, hallucination mitigations, and human-in-the-loop intervention patterns.

Hosting architectures, latency reduction, cost management, rate limiting, and observability in production environments.