Agentic solutions leverage multiple autonomous agents (usually SLM) to achieve goals collaboratively. These systems distribute tasks across agents that operate individually or collectively to solve complex problems.
Agent Interactions
How do Agents Interact?
- Horizontal Collaboration: Agents with local goals coordinate to achieve a shared objective.
- Hierarchical Collaboration: Primary agents oversee specialized agents to manage complex tasks effectively.
Types of Agentic Solutions
Chat Experiences:
Reactive Solutions (Ask Approach):
Systems like chatbots and retrieval-augmented generation (RAG) tools respond to user queries.
Autonomous Solutions (Do Approach):
Agents perform tasks proactively, e.g., drafting documents or scheduling meetings.
Understanding Agents
Core Components:
- Tools: Resources such as APIs, databases, or GitHub.
- Strategy: Techniques like self-criticism, chain of thought (CoT), and planning to improve reasoning.
- States: Memory, context tracking, and microservices for modularity.
- Goals: Specific objectives defined for each agent.
Agent Planning and Interaction
- Planning: Agents plan operations, such as managing workflows in a support center.
- Agent Collaboration: Agents align their individual goals with shared objectives to enhance system performance.
Compounding Systems
Multi-Agent Systems:
These systems reduce reliance on extensive prompt engineering by compartmentalizing tasks across specialized agents.
Example: A writer agent drafts content, while a reviewer agent ensures quality, both operating within defined scopes.
Business Process Integration
Workflow:
- Identify a business problem.
- Define personas (agents) required.
- Develop an agentic workflow.
Agentic Architectures:
- Vertical: Hierarchical structures for task delegation.
- Horizontal: Collaborative structures with high feedback loops.
- Mixed: Combines vertical delegation with horizontal collaboration.
Vertical Example: A primary agent delegates tasks to lower-level agents for execution.
The Orleans Framework
A framework for building distributed applications in .NET.
- Grains: Individual agents performing specific tasks.
- Silos: Distributed nodes managing grains.
- Clusters: Collections of silos for scalability.
Problem Solving with Agents
Defining Roles:
Agents can model specific business functions. Role clarity enhances the effectiveness of these systems.
Benefits of Using Agents
- Performance Gains: Task parallelization enhances throughput.
- Developer Abstraction: Modular design simplifies system understanding and debugging.
- Workflow Integration: Aligns AI agents with organizational processes.
Example Use Cases
- IT Helpdesk Agent: Automates troubleshooting and network access requests.
- Device Refresh Agent: Manages hardware upgrades and approvals.
- Lead Generation Agent: Identifies and researches potential leads.
- Budget Management Agent: Reviews financial data and aids in planning.
- Customer Support Agent: Triage support issues for faster resolution.
- Project Tracker Agent: Tracks project milestones and budget compliance.