AI-Agentic-Design-Patterns-with-AutoGen

Implements a message-passing architecture where multiple specialized agents exchange messages in a structured conversation loop, with AutoGen's ConversableAgent class managing state, message history, and turn transitions. Each agent maintains its own system prompt, tools, and LLM configuration, enabling heterogeneous agent teams to collaborate on complex tasks through natural language exchanges rather than rigid function calls.

Enables agents to evaluate their own outputs against task requirements and iteratively improve through a reflection pattern where one agent (e.g., critic) provides structured feedback to another (e.g., executor). Implemented via agent-to-agent message exchanges where critique agents use custom prompts to assess correctness, completeness, and quality, feeding results back into the main agent's context for refinement.

Enables creation of specialized agents for specific domains (financial analysis, customer service, coding) by defining role-specific system prompts that encode domain expertise, terminology, and reasoning patterns. Agents inherit domain knowledge through their system prompt and can be further customized with domain-specific tools and knowledge bases, allowing agents to reason and act as domain experts.

Automates customer onboarding processes by orchestrating multiple agents (intake agent, verification agent, setup agent) that collaborate to gather information, verify details, and configure customer accounts. Agents exchange information through conversation, with each agent responsible for a specific onboarding step, and the workflow adapts based on customer responses and verification results.

Provides a mechanism for agents to declare and invoke external tools (APIs, code execution, databases) through a schema-based function registry. Tools are registered as Python functions with JSON schema descriptions, and agents can dynamically call them by name with arguments; AutoGen handles schema validation, function invocation, and result serialization back into the conversation context.

Enables agents to generate Python code as part of their reasoning process and execute it in an isolated sandbox environment (via exec() with restricted globals/locals or containerized execution). Generated code results are captured and fed back into the agent's conversation context, allowing agents to use code as a tool for computation, data analysis, or problem-solving without breaking the main application.

Implements planning patterns where a high-level planner agent breaks down complex tasks into subtasks and delegates them to specialized worker agents, with the planner coordinating results and adapting the plan based on feedback. Uses a hierarchical conversation structure where the planner maintains a task graph or plan representation and routes subtasks to appropriate agents, collecting and synthesizing their outputs.

Manages the conversation state across multiple agent turns by maintaining a message history (list of agent messages with roles, content, and metadata) and providing mechanisms to retrieve, filter, and summarize past context. Agents can access the full conversation history to maintain coherence, and the framework provides utilities for context windowing (keeping only recent messages) and optional persistence to external storage.

Abstracts away LLM provider differences through a unified configuration interface where agents can be configured with different model providers (OpenAI, Anthropic, Azure OpenAI, local Ollama) and parameters (temperature, max_tokens, system prompt) without changing agent code. Handles provider-specific API calls, error handling, and response parsing transparently.

Implements mechanisms to control when agent conversations end through configurable stopping conditions (max turns, specific keywords, agent consensus, external signals). Agents can signal completion through special messages or return values, and the framework evaluates stopping conditions after each turn to determine whether to continue the conversation or terminate.

Enables agents to collaboratively create content (blog posts, reports, code documentation) through a workflow where a writer agent generates initial content, reviewer agents provide feedback, and the writer iterates based on critique. Implements a content creation pipeline where agents exchange drafts and feedback through the conversation, with each iteration improving quality based on specific criteria.

Enables agents to play games (chess, strategy games) by implementing game state management and turn-based interaction where agents receive the current game state, generate moves, and receive feedback on move validity and game progression. Agents use reasoning to plan moves and adapt strategy based on opponent actions, with the framework handling game rule enforcement and state transitions.