Agentic Workflows
Master the design and implementation of AI agents that can perform multi-step tasks, interact with tools, and execute complex workflows to solve real-world problems.
What are Agentic Workflows?
Agentic workflows represent a paradigm shift in AI systems, enabling them to autonomously execute multi-step tasks, make decisions, and integrate with tools to solve complex problems without constant human intervention.
Tool Integration
Agents can interact with external tools and APIs, extending their capabilities beyond language processing. This allows them to retrieve information, execute code, analyze data, or control external systems.
Multi-Step Reasoning
Agents break down complex tasks into manageable steps, formulating plans and adjusting their approach based on intermediate results to reach specific goals.
Feedback Loops
The ability to evaluate outputs, iterate on solutions, and refine approaches enables agents to improve their performance through continuous learning and adaptation.
Agent Architecture Components
Modern AI agents are composed of several key components that work together to enable complex reasoning, decision-making, and action.
Core LLM
The foundation model that processes inputs, generates outputs, and powers the agent's reasoning capabilities. The LLM serves as the "brain" that coordinates all other components.
Tool Library
A collection of specialized functions the agent can call to perform specific tasks, such as calculations, data retrieval, or interaction with external systems.
Memory System
Enables the agent to store and retrieve information across interactions, maintaining context and building on previous results throughout a multi-step workflow.
Planning Module
Helps the agent break down complex tasks into manageable steps, creating execution plans to achieve specific goals efficiently.
Orchestration Layer
Manages the flow of information between components, coordinating the agent's internal processes and external tool interactions for structured workflow execution.
Guardrails System
Provides safety checks, validation rules, and constraints that ensure the agent operates within defined boundaries and produces reliable results.
Tool Integration
Tool integration allows agents to transcend language capabilities by connecting to external services, APIs, and functions.
Function Calling
The ability for agents to correctly identify when to use specific functions, provide appropriate parameters, and interpret returned results. This enables integration with code execution, databases, APIs and more.
Tool Selection
Agents must determine which tools in their toolkit are appropriate for specific sub-tasks, requiring contextual understanding and tool capabilities awareness.
Error Handling
Robust agents can interpret error messages, attempt repairs, and implement fallback strategies when tool execution fails, ensuring workflow resilience.
Common Tool Categories
Information Retrieval: Search engines, knowledge bases, vector stores
Code Execution: Python interpreters, REPL environments
Data Access: Database queries, file system operations
API Calls: Weather services, stock data, maps
Specialized Functions: Math calculations, date operations
Data Analysis: Statistics tools, visualization generators
Multi-Step Reasoning
Effective agents break down complex problems into logical steps, pursuing solutions through structured reasoning and planning.
Reasoning Patterns in Agentic Workflows
Chain-of-Thought
Breaking down complex problems into a sequence of logical steps, making the reasoning process explicit and traceable. This leads to more accurate results for complex reasoning tasks.
Tree-of-Thought
Exploring multiple reasoning paths simultaneously and evaluating different approaches before selecting the most promising solution path.
Reflection Loops
Evaluating generated outputs, identifying potential errors or improvements, and iterating on solutions to enhance quality and accuracy.
Examples of Multi-Step Reasoning
Complex Task: "Find the best restaurants near Central Park with vegetarian options and make a reservation for tomorrow"
- Use geolocation API to determine the coordinates of Central Park
- Query restaurant search API for establishments within 1km of those coordinates
- Filter results to include only those with vegetarian menu options
- Sort filtered results by rating (descending)
- Check availability of top 3 restaurants for tomorrow using reservation API
- Make reservation at highest-rated available restaurant
- Return confirmation details to user
Execution Traces
Execution traces provide a detailed record of an agent's thought process, tool usage, and decision-making throughout a workflow.
Anatomy of an Execution Trace
An execution trace documents each step of an agent's workflow, including:
Thought Process: The agent's internal reasoning
Tool Selection: Which tool was chosen and why
Tool Input: Parameters provided to the tool
Tool Output: Results returned from the tool
Next Steps: Decisions based on tool outputs
Error Handling: Responses to failures or exceptions
Benefits of Execution Traces
Transparency
Provides visibility into how the agent reached its conclusions, making the system more trustworthy and understandable.
Debugging
Helps identify specific points of failure in complex workflows and understand why certain decisions were made.
Improvement
Enables iterative refinement of agent behavior by analyzing patterns in successful and unsuccessful executions.
Lab 1: Agent Builder UI
Design your own AI agent by selecting capabilities, tools, and reasoning patterns to solve specific tasks.
Agent Builder
Core Capabilities
Available Tools
Reasoning Pattern
Agent Configuration
Your agent configuration will appear here after building...
Execution Preview
Build your agent above to see how it would approach your task...
Lab 2: Mock API Integration
Experiment with connecting AI agents to external APIs to extend their capabilities with real-time data.
Available APIs
API Configuration
Function Definition
function getWeather(location, units = 'metric') {
// This function would connect to a weather API
// and return current conditions for the specified location
return fetch(`https://api.example.com/weather?q=${location}&units=${units}`)
.then(response => response.json());
}
API Response
Click "Test API Call" to see the mock response...
Agent Integration
Agent Response
Lab 3: Execution Trace Visualization
Visualize and analyze how agents step through complex workflows with interactive trace exploration.
Select from pre-defined scenarios to see how an AI agent processes multi-step tasks using tools and reasoning.
Execution Trace
Select and run a scenario to see the execution trace
Run a scenario to visualize the agent's execution trace
Analysis
Steps: 0
Tool Calls: 0
Reasoning Depth: N/A
Execution Time: 0.0s
Final Result
Lab 4: Tool Integration Playground
Experiment with various tools and see how they enhance an agent's capabilities for solving real-world problems.
Tool Library
Select tools to make available to the agent for solving tasks.
Web Search
Find information online
Math
Perform calculations
Code Execution
Run Python code
Calendar
Check schedules
Weather
Get weather data
Translate
Translate languages
Task Description
Tool Configuration
Agent Workflow
Final Response
Key Takeaways
Tool Integration Powers
Connecting AI models to external tools and APIs dramatically expands their capabilities beyond language processing, enabling real-world interaction and problem-solving.
Multi-Step Excellence
Complex problems require structured thinking. Agents that break down tasks into logical steps and maintain context across those steps deliver more reliable outcomes.
Transparency Matters
Execution traces provide crucial visibility into agent reasoning, enhancing trust, enabling debugging, and providing opportunities for continuous improvement.
Unit Progress
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