Author(s): Amir Javaheri Originally published on Towards AI. Part 1 of the Agentic Workflow Series Source: Image by Pete Linforth from Pixabay Introduction In the rapidly evolving world of software development, we’re transitioning from static applications to dynamic, intelligent systems — agents. These advanced AI-powered entities are not just reactive but are proactive, capable of perceiving their environment and autonomously acting to fulfill complex objectives. This paradigm shift introduces agentic workflows — sophisticated sequences where agents plan, decide, and execute tasks, revolutionizing our technological interactions. To tap into this potential, structured approaches are crucial. Agentic design patterns, akin to traditional software patterns, provide robust frameworks for crafting effective agent behaviors, thus driving the creation of dynamic and responsive systems. Agentic Design Patterns: Structuring Intelligence Understanding foundational agentic patterns is essential before delving into the specifics of the Tool Use Pattern. Each pattern addresses key aspects of agent functionality: The Reflection Pattern: Through iterative self-evaluation, the Reflection Pattern allows agents to learn from experience, driving ongoing performance enhancements. Specifically, it involves the agent analyzing the consequences of its previous actions, identifying errors or areas for improvement, and adjusting its internal model or strategies accordingly. The Planning Pattern: The Planning Pattern empowers agents to proactively chart their course, breaking down complex goals into manageable steps. It involves generating and refining action sequences, anticipating potential obstacles, and adapting to changing circumstances. By creating a roadmap, agents can navigate intricate tasks with greater efficiency and foresight, ensuring they move strategically towards their objectives. The Memory Pattern: Vital for continuity, this pattern allows agents to store and retrieve information, ensuring informed decisions based on context. The Multi-Agent Pattern: Orchestrates the interaction of multiple agents working towards a unified goal, ensuring cohesive operation and enhanced problem-solving capabilities. If you’d like to dive deeper into these topics, feel free to follow me on Medium for more insights. The Tool Use Pattern: Empowering Agents Central to expanding an agent’s capabilities is the Tool Use Pattern. This pattern equips agents to integrate and utilize external tools, broadening their operational scope beyond inherent capabilities, which will be explored through detailed workflows and real-world applications. Step-by-Step Breakdown of Workflow 1. User Input Reception: Setting the Stage The journey begins with user input, the catalyst for the agent’s actions. This can range from a simple question to a complex, multi-faceted request. The agent receives this natural language input, preparing it for the intricate process of understanding and action. 2. Intent Recognition: Unveiling User Goals At the heart of understanding lies intent recognition. The system examines the user’s input to decode its underlying meaning. This involves: Identifying potential “intents,” each representing a user goal, with associated confidence scores. Extracting relevant parameters to provide context. For instance, “Find climate change info and save it” yields “search” (query: “climate change”) “file_operation” (operation: “write,” path: “some_file”) intents. A sophisticated intent recognition system handles ambiguity, multiple intents, structured parameter extraction, and accurate confidence assignment. 3. Tool Selection: Choosing the Right Instruments With the user’s intent clarified, the agent selects the appropriate tools. This selection, driven by LLMs, machine learning, or rule-based systems, involves: Mapping intents to available tools. Filtering irrelevant or low-confidence tools. Determining optimal tool execution sequences. Preparing tool-specific parameters. The process considers intent confidence, tool capabilities, tool dependencies, and user preferences. 4. Tool Input Generation: Bridging the Gap This crucial step transforms raw parameters into the precise format required by each tool. This ensures seamless tool execution through: Parameter Formatting: Converting extracted parameters into tool-specific formats (e.g., dates, codes). Parameter Validation: Ensuring parameter presence and validity (e.g., valid cities, future dates). Parameter Augmentation: Adding default or derived parameters (e.g., origin city, passenger count). Parameter Transformation: Performing necessary conversions (e.g., city to airport code, timezone adjustments). Context Integration: Incorporating conversation history or system state. Error Prevention: Catching parameter issues before execution. Security Filtering: Sanitizing inputs to prevent vulnerabilities. Example: Booking a Flight Let’s walk through an example to see how each step is completed before the tool is executed. User input: “Book me a flight to Tokyo next Friday returning Sunday, economy class” 2. Intent Recognition: { "intent": "book_flight", "confidence": 0.95, "parameters": { "destination": "Tokyo", "departure_date": "next Friday", "return_date": "Sunday", "cabin_class": "economy class" }} 3. Tool Selection: { "selected_tool": "FlightBookingTool", "confidence": 0.98, "intent_mapping": "book_flight → FlightBookingTool"} 4. Tool Input Generation 4.1 Parameter Formatting{ "destination": "Tokyo", "departure_date": "2023-05-19", "return_date": "2023-05-21", "cabin_class": "economy"}4.2 Parameter Validation{ "validation_status": "valid", "checks_passed": [ "destination is a valid city", "departure_date is in the future", "return_date is after departure_date", "cabin_class is a valid option" ], "warnings": []}4.3 Parameter Augmentation{ "destination": "Tokyo", "departure_date": "2023-05-19", "return_date": "2023-05-21", "cabin_class": "economy", "origin": "New York", "passengers": 1, "preferred_airlines": ["ANA", "JAL", "United"], "meal_preference": "vegetarian", "checked_bags": 1}4.4 Parameter Transformation{ "departure_date": "2023-05-19T00:00:00-04:00", "return_date": "2023-05-21T00:00:00-04:00", "cabin_class": "Y", "origin": "JFK", "destination": "HND", "passengers": 1, "preferred_airlines": ["ANA", "JAL", "United"], "meal_preference": "vegetarian", "checked_bags": 1}4.5 Context Integration and 4.6 Error Prevention{ "execution_status": "proceed_with_warning", "parameters": { "departure_date": "2023-05-19T00:00:00-04:00", "return_date": "2023-05-21T00:00:00-04:00", "cabin_class": "Y", "origin": "JFK", "destination": "HND", "passengers": 1, "preferred_airlines": ["United", "ANA", "JAL"], "meal_preference": "vegetarian", "checked_bags": 1, "preferred_area": "Shinjuku, Tokyo", "loyalty_programs": { "United": "UA123456789" }, "trip_purpose": "business" }, "warnings": [ "Your trip is only 2 days. Given the long flight to Tokyo, you might want to consider a longer stay." ]} 5. Tool Execution: Putting Tools into Action The agent executes the selected tools in sequence, each receiving its designated parameters. This involves: Tool performance of its designated function. Result capture, including success/failure status. Structured output data for later use. Feedback loop. Graceful error handling. Tools range from information retrieval to code execution, each producing a structured result with status, message, data, and error information. 6. Response Generation: Crafting the Final Output After tool execution, the agent generates a coherent response, considering: Original user input. Recognized intents. Results from executed tools. The response generator handles successful and failed executions, mixed results, and cases needing a response without tool execution. It prioritizes relevant information, formats tool outputs, provides context, and suggests next steps. Use Cases of the Tool Use Pattern: Code Execution: Automates tasks and solves computations through direct […]
↧