Skill Guide

Agentic workflow design including tool-use, function calling, ReAct patterns, and multi-agent orchestration

The systematic design of autonomous AI agent systems that decompose tasks, select and invoke external tools or functions via APIs, and coordinate multiple specialized agents to execute complex workflows.

This skill enables the creation of scalable, self-directed AI solutions that automate complex, multi-step business processes, directly increasing operational efficiency and enabling new product capabilities that were previously impossible with single-shot LLM queries.

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9.2 Avg Demand

15% Avg AI Risk

How to Learn Agentic workflow design including tool-use, function calling, ReAct patterns, and multi-agent orchestration

Start by mastering the fundamentals of prompt engineering for chain-of-thought reasoning. Study the core concepts of tool definition, JSON schema for function calling, and the basic ReAct (Reasoning + Acting) loop using frameworks like LangChain or LlamaIndex. Build simple single-agent applications that call one or two external tools (e.g., a calculator, a web search API).

Progress to designing multi-step agent workflows with conditional logic and error handling. Implement a complete ReAct agent with a defined toolset, focusing on robust tool selection and output parsing. Common mistakes include poor tool description writing, which leads to incorrect selection, and failing to implement proper guardrails and fallback mechanisms for when tools fail.

Master the architecture of multi-agent systems (MAS). Focus on defining agent roles (orchestrator, specialist, critic), communication protocols (direct messaging, shared blackboard), and orchestration patterns (hierarchical, collaborative). Design systems for strategic task delegation, consensus building among agents, and integrating human-in-the-loop checkpoints for critical decisions.

Practice Projects

Beginner

Project

Build a Research Assistant Agent

Scenario

Create an agent that can take a user's research question, use a search tool to find relevant sources, and use a summarization tool to produce a concise report.

How to Execute

1. Define two tools: `web_search(query)` and `summarize_text(text)`. 2. Implement a ReAct loop using an LLM that can reason about when to use each tool. 3. Format the agent's final output as a structured report with sources. 4. Test with a variety of queries to assess tool selection accuracy.

Intermediate

Project

Design a Customer Support Ticket Router

Scenario

Build an orchestrator agent that receives a support ticket, classifies its intent and severity, and delegates it to the correct specialist agent (e.g., Billing, Technical, Sales) for initial response drafting.

How to Execute

1. Create a taxonomy of intents and severity levels. 2. Build an orchestrator agent with tools for ticket classification. 3. Build 2-3 specialist agents, each with a unique system prompt and access to different knowledge bases or action tools (e.g., 'check_invoice_status'). 4. Implement the handoff protocol where the orchestrator passes context to the selected specialist and returns its final response.

Advanced

Project

Implement a Software Development Swarm

Scenario

Architect a multi-agent system where a 'Product Manager' agent breaks down a feature spec into user stories, a 'Coder' agent writes code for each story, and a 'QA Tester' agent writes and executes tests, with a 'Project Lead' agent orchestrating the workflow and resolving conflicts.

How to Execute

1. Define strict roles, goals, and communication interfaces for each agent. 2. Implement an orchestration layer (e.g., using CrewAI or AutoGen) to manage the task queue and agent interactions. 3. Design state management for the shared codebase and test results. 4. Integrate a verification loop where the 'QA Tester' provides feedback to the 'Coder' until all tests pass.

Tools & Frameworks

Software & Platforms

LangChain / LangGraphLlamaIndexOpenAI Function Calling / Assistants APIAutoGenCrewAI

These are the primary development frameworks. LangChain/LangGraph provide foundational abstractions for chains and agents. LlamaIndex excels at data-centric agents. The OpenAI APIs provide native function calling. AutoGen and CrewAI are specialized multi-agent frameworks for orchestrating conversations and role-based teams.

Architectural Patterns & Mental Models

ReAct PatternPlan-and-Execute PatternMulti-Agent Debate (MAD)Hierarchical Task Network (HTN)

These are the core design paradigms. ReAct is the standard for single-agent tool use. Plan-and-Execute separates planning from acting for more complex tasks. MAD is used for agents to critique each other to improve output quality. HTN is used in advanced systems to decompose large tasks into executable subtasks for delegation.

Interview Questions

Answer Strategy

The candidate should demonstrate understanding of the scalability and cognitive limits of single agents. A strong answer will reference context window limits, complexity of reasoning chains, or need for parallel execution. They should propose a multi-agent architecture with specific roles and a communication protocol. Sample answer: 'A single ReAct agent would fail when tasked with independently writing, testing, and deploying a complex code module, as the reasoning chain becomes too long and error-prone. I would redesign it as a multi-agent system with a Planner agent that decomposes the task, a Coder agent that implements solutions, and a Tester agent that validates them. The Planner would orchestrate the workflow, allowing for focused expertise and iterative refinement between Coder and Tester until criteria are met.'

Answer Strategy

The interviewer is testing the candidate's approach to quality assurance for non-deterministic systems. A strong answer will include quantitative metrics and structured testing. Sample answer: 'I evaluate agentic systems through a layered testing strategy: unit tests for individual tool integrations, scenario-based tests for the agent's decision logic using a predefined benchmark set of tasks, and monitored pilot deployments with human reviewers to assess end-to-end reliability. Key metrics include task completion rate, tool call accuracy, average steps to completion, and frequency of human intervention required.'