Building AI Agents for Beginners

Understand the core loop: Perception → Reasoning → Action → Feedback. Moving beyond static chat completion.

• LLM APIs, function calling schemas, and structured JSON outputs
• ReAct (Reason + Act) loops and reflection patterns
• Connecting models to local tools (file system, calculator, web search)
• Error recovery and self-healing agent prompts

Equipping your agents with dynamic knowledge and long-term memory using vector search and local databases.

• Vector embeddings, similarity matching, and hybrid search
• Document parsing, chunking strategies, and token budget management
• Persistent memory stores (Short-term chat history vs. long-term recall)
• Grounding agents to prevent hallucinations

When one agent isn't enough: Orchestrating specialized worker agents with supervisor review loops.

• Supervisor-worker architecture and task decomposition
• Model Context Protocol (MCP) integrations
• Pair programming agents: Builder agent + QA verifier agent
• State machines and deterministic workflows with agent flexibility

Packaging your agent as a real product, containerizing, deploying to Fly.io, and taking payments via Stripe.

• Deploying agents via Docker on Fly.io edge infrastructure
• Secure credential management using Azure Key Vault
• Integrating Stripe payments and automated customer follow-ups
• Observability, logging agent trajectories, and token cost tracking