Database

Core patterns for building AI agent systems: the observe-think-act loop, ReAct pattern implementation, tool-use cycles, memory systems (short-term and long-term), and planning strategies. Covers how to structure an agent's main loop, manage state between iterations, and wire together perception, reasoning, and action into a reliable autonomous system.

Handling failures in AI agent systems: retry strategies with backoff, fallback tools, graceful degradation, human-in-the-loop escalation, stuck-loop detection, and context recovery after crashes. Covers practical patterns for making agents robust against tool failures, API errors, and reasoning dead-ends.

Testing and evaluating AI agents: trajectory evaluation, task completion metrics, tool-use accuracy measurement, regression testing, benchmark suites, and A/B testing agent configurations. Covers practical approaches to measuring whether agents are working correctly and improving over time.

Comparison of major AI agent frameworks: LangGraph, CrewAI, AutoGen, Semantic Kernel, and Claude Agent SDK. Covers when to use each framework, their trade-offs, core patterns, practical setup examples, and migration strategies between frameworks.

Safety and control systems for AI agents: input and output validation, action authorization, rate limiting, cost controls, content filtering, scope restriction, and audit logging. Covers practical implementations for keeping agents within bounds while maintaining their usefulness.

Memory systems for AI agents: conversation history management, summarization strategies, vector-based long-term memory, entity memory, episodic memory, and memory retrieval patterns. Covers practical implementations for giving agents persistent, searchable memory across sessions and within long-running tasks.

Planning strategies for AI agents: chain-of-thought prompting, tree-of-thought exploration, plan-and-execute patterns, iterative refinement, task decomposition, and goal tracking. Covers practical implementations that make agents more reliable at complex, multi-step tasks by thinking before acting.

Building agents specifically with the Claude API: extended thinking for complex reasoning, tool use patterns, computer use for browser/desktop automation, multi-turn conversation management, crafting system prompts for agents, and streaming agent responses. Covers Claude-specific features and best practices for building reliable autonomous agents.

Orchestrating multiple AI agents working together: supervisor patterns, swarm architecture, handoff protocols, agent-to-agent communication, and agent specialization. Covers practical patterns for splitting complex tasks across coordinated agents, managing shared state, and routing work to the right specialist agent.

Implementing tool and function calling across Claude, OpenAI, and Gemini APIs. Covers schema design best practices, parallel tool calls, error handling, tool result formatting, dynamic tool registration, and patterns for building composable tool sets that agents can use reliably.