AI Agent Builder learning path: everything to study, topic by topic

Working with LLM provider APIs including OpenAI, Anthropic, and Google, understanding authentication, rate limiting, pricing models, and choosing the right API for agent workloads

Designing system prompts and instruction sets for autonomous agents, controlling temperature and sampling parameters, structured output formats, and prompt chaining strategies

Defining tools with JSON Schema, understanding the function calling lifecycle across providers, handling tool results, and designing tool interfaces for agents

Choosing the right model for agent tasks based on context window size, reasoning ability, cost per token, multimodal capabilities, and latency requirements

Implementing the Reasoning plus Acting pattern, building Thought-Action-Observation loops, managing loop termination conditions, and handling multi-step agent execution

Comparing agent frameworks including LangGraph, CrewAI, OpenAI Agents SDK, Claude Agent SDK, Pydantic AI, and Google ADK, understanding their tradeoffs and ideal use cases

Implementing working memory, session memory, and long-term memory for agents using tools like Mem0, LangMem, and Letta, including context window compression strategies

Deciding when a single agent suffices versus when to decompose into multiple agents, understanding cost and complexity tradeoffs, and avoiding premature multi-agent architectures

Working with vector databases including Pinecone, Weaviate, Chroma, and pgvector, understanding embedding models, similarity search, and index optimization

Implementing fixed-size, semantic, and recursive chunking strategies, enriching chunks with metadata, handling diverse document formats, and optimizing chunk size for retrieval

Building hybrid retrieval pipelines combining BM25 and dense retrieval, implementing re-ranking, query transformation, HyDE, and multi-stage retrieval architectures

Measuring RAG quality with precision, recall, and groundedness metrics, identifying and fixing common failure modes, and iterating on retrieval and generation quality

Implementing supervisor, hierarchical, swarm, and graph-based multi-agent patterns, understanding when each pattern applies and their operational characteristics

Understanding Model Context Protocol (MCP), Agent-to-Agent protocol (A2A), message passing patterns, shared state approaches, and standardized agent interoperability

Implementing agent-to-agent handoffs with context transfer, routing logic, fallback strategies, and human-in-the-loop escalation patterns

Implementing plan-and-execute patterns, Reflexion loops, self-critique mechanisms, and enabling agents to learn from mistakes within and across sessions

Understanding direct and indirect prompt injection attacks, implementing defense layers, applying OWASP LLM Top 10 guidelines, and building input sanitization for agents

Evaluating agent performance using benchmarks like SWE-bench, GAIA, and WebArena, building evaluation datasets, integrating evals into CI/CD, and measuring agent reliability

Implementing output filtering and content moderation, handling PII detection and redaction, understanding EU AI Act requirements, and building guardrail systems for agents

Conducting adversarial testing of agent systems, simulating attack scenarios, testing memory poisoning defenses, and building automated red teaming pipelines

Implementing distributed tracing for agent execution using LangSmith, Langfuse, and Arize, visualizing execution graphs, and building monitoring dashboards for agent health

Deploying agents in serverless and containerized environments, externalizing state for horizontal scaling, building CI/CD pipelines for agent code, and managing model versioning

Optimizing token usage, implementing prompt caching, routing between models based on task complexity, batching requests, and projecting costs at scale

Implementing retry logic with exponential backoff, graceful degradation when LLM providers fail, circuit breaker patterns, and building resilient agent execution pipelines

Building custom MCP servers, designing tool schemas for complex operations, implementing authentication and authorization for tool access, and publishing reusable tool packages

Designing agent workflows for regulatory compliance, enterprise integrations, healthcare, legal, and financial domains with appropriate human oversight and audit trails

Deciding when to fine-tune versus prompt-engineer, preparing training datasets, understanding RLHF and DPO alignment techniques, and evaluating fine-tuned model quality

Designing multi-tenant agent platforms, building agent marketplaces, implementing governance frameworks for organizational agent deployment, and managing agent lifecycles at enterprise scale