Course · 10 modules · 42 lessons · 272 min

Building a Multi-Skill AI Agent

Hands-on guide to building an AI agent with multiple skills — architecture, tool design, orchestration, error handling, and a capstone research agent project.

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№ 01Agent Architecture Foundations
01The Agent Runtime LoopThe agent runtime loop is the core execution cycle where the agent repeatedly reasons about what to do next, executes a skill, observes the result, and decides whether to continue or stop.6 min→02Anatomy of a Multi-Skill AI AgentA multi-skill agent is an LLM-powered system that dynamically selects and sequences distinct capabilities to accomplish complex, multi-step goals.7 min→03Choosing Your FrameworkThe right framework for building a multi-skill agent depends on your complexity needs, control requirements, and team experience — options range from raw API loops to full orchestration platforms.7 min→04The Skill AbstractionA skill is a self-contained, well-defined capability with clear inputs, outputs, and side effects that an agent can invoke as a building block for complex tasks.6 min→
№ 02Defining Skills As Tools
01Building Action SkillsAction skills modify external state -- writing files, calling APIs, updating databases, sending messages -- and require idempotency, confirmation patterns, and dry-run modes to prevent irreversible mistakes.10 min→02Building Retrieval SkillsRetrieval skills search and fetch information from external sources -- web search, databases, file systems, and vector stores -- giving the agent access to knowledge beyond its training data.8 min→03Designing Effective Tool SchemasWell-designed tool schemas with descriptive names, clear descriptions, typed parameters, and sensible defaults are the single biggest factor in whether an LLM reliably selects and invokes the right tool.7 min→04Input Validation and Type SafetyValidating tool inputs before execution prevents bad data from cascading through tool chains, turning silent failures into clear error messages the LLM can understand and correct.7 min→05Output ContractsConsistent, structured tool outputs with clear success/error distinction and metadata enable the LLM to reliably parse results and make confident decisions about what to do next.8 min→
№ 03The Reasoning Core
01Chain-of-Thought for Multi-Step TasksExplicit step-by-step reasoning (think, plan, act) dramatically reduces errors when agents must chain multiple tool calls to complete complex tasks.8 min→02Skill Selection ReasoningThe LLM's ability to choose the right tool for each step depends on how well tool descriptions match user intent, and description quality is the single biggest lever for selection accuracy.7 min→03System Prompt as Agent DNAThe system prompt is the single most influential piece of code in an AI agent, defining its identity, capabilities, constraints, and behavior in every interaction.8 min→04When to StopAn agent without well-defined termination conditions will loop forever, burning money and producing garbage — knowing when to stop is as important as knowing what to do.8 min→
№ 04State And Memory Across Steps
01Context Window PressureEvery agent step consumes context window space, and when the window fills up, the agent must either summarize, prune, or fail — making token budgeting a core engineering concern for long-running agents.8 min→02Conversation as Working MemoryThe message history in an agent loop functions as working memory, accumulating context that shapes every subsequent reasoning step and tool invocation.7 min→03Persistent Memory Across SessionsWorking memory vanishes when an agent session ends; persistent memory uses checkpointing, databases, and long-term stores to let agents remember information across separate invocations.7 min→04Structured State ManagementWhen conversation history alone cannot reliably track complex agent state, typed state objects and explicit key-value stores give agents a structured, programmatically accessible memory that survives context window pressure.7 min→
№ 05Task Decomposition And Planning
01Adaptive ReplanningAdaptive replanning enables agents to revise their execution plan on the fly when reality diverges from expectations, balancing persistence with flexibility.6 min→02Breaking Complex Tasks into StepsAgents tackle complex requests by recursively decomposing them into atomic sub-tasks arranged in a dependency-aware hierarchy.7 min→03Dependency Graphs for Skill ExecutionModeling task steps as a directed acyclic graph (DAG) enables agents to identify parallelizable work and execute skills in optimal order.6 min→04Plan-Then-Execute PatternThe plan-then-execute pattern separates task planning from task execution into two distinct phases, producing more reliable and transparent agent behavior.6 min→
№ 06Skill Orchestration Patterns
01Conditional BranchingConditional branching lets agents dynamically route execution based on intermediate results, choosing different skills or strategies depending on what the data looks like at runtime.4 min→02Human-in-the-Loop CheckpointsHuman-in-the-loop checkpoints pause agent execution at critical decision points to get human approval before proceeding with high-stakes or irreversible actions.4 min→03Parallel Skill ExecutionRunning multiple independent skills concurrently using asyncio and LangGraph fan-out patterns dramatically reduces agent latency when tasks have no data dependencies.6 min→04Sequential Skill ChainsSequential skill chains execute tools in strict order where each step's output feeds directly into the next step's input, forming the simplest and most predictable orchestration pattern.6 min→05The Supervisor PatternThe supervisor pattern uses a meta-agent to coordinate specialized worker agents, routing tasks to the right expert and aggregating their results into a coherent response.5 min→
№ 07Error Handling And Recovery
01Error Categories in Agent SystemsA taxonomy of the four major error categories in AI agent systems — tool execution failures, LLM reasoning errors, state corruption, and environmental errors — along with their frequency, severity, and appropriate handling strategies.6 min→02Graceful DegradationStrategies for maintaining useful agent behavior when one or more skills are unavailable, including fallback chains, capability degradation matrices, and user notification patterns.6 min→03Retry Strategies and BackoffA guide to when and how to retry failed operations in agent systems, covering exponential backoff with jitter, idempotency considerations, and the critical distinction between retryable and non-retryable errors.7 min→04Self-Correction and ReflectionTechniques for building agents that detect their own mistakes and fix them, including output validation, reflection prompts, the Reflexion pattern, and post-tool-call verification — typically improving task success rates by 10–25%.7 min→
№ 08Testing Multi Skill Agents
01Evaluation with Test SuitesHow to build a structured evaluation harness of 20-50 tasks to measure agent performance using automated scoring methods including exact match, LLM-as-judge, and rubric-based assessment.5 min→02Integration Testing Skill ChainsHow to test that agent skills work correctly together by validating data flow between steps, conditional branching logic, and error propagation across multi-skill chains.5 min→03Regression Testing for AgentsTechniques for ensuring that changes to an agent do not break existing capabilities, including golden test sets, trajectory snapshot testing, statistical regression detection, and CI/CD integration.5 min→04Unit Testing Individual SkillsHow to test each agent skill in isolation using mocks, input validation tests, output format assertions, and edge case coverage — forming the base of the testing pyramid for AI agents.6 min→
№ 09Production Deployment
01Cost Tracking and OptimizationManaging and minimizing the financial cost of running multi-skill AI agents in production through systematic tracking, budgeting, and optimization strategies.6 min→02Latency Budgets and TimeoutsLatency budgets decompose end-to-end response time targets into per-step limits, ensuring multi-skill agents deliver results within acceptable time frames.7 min→03Observability and TracingObservability for AI agents means capturing structured traces of every reasoning step, tool call, and decision so you can understand, debug, and optimize agent behavior in production.7 min→04Scaling Agent WorkloadsScaling multi-skill agents requires managing concurrent sessions, queuing task execution, enforcing rate limits, and distributing work across multiple processes to serve hundreds or thousands of simultaneous users.8 min→
№ 10Capstone Build A Research Agent
01Implementing the Skill SetStep-by-step implementation of the five core skills -- web search, page reading, summarization, fact checking, and report writing -- each with typed interfaces and error handling.4 min→02Project Overview and RequirementsThe capstone project is a fully functional research agent that takes a topic, searches the web, reads and summarizes articles, cross-references facts, and produces a structured report.6 min→03Running and IteratingRunning a multi-skill agent on real tasks exposes failure modes that only emerge in practice — iterating on the system prompt, error handling, and skill implementations transforms a prototype into a reliable tool.7 min→04Wiring the Agent GraphAssembling the five research skills into a LangGraph state machine with typed state, conditional routing, and a system prompt that guides the research workflow.4 min→