Skip to content

AGENT_HANDOFFS_IMPLEMENTATION.md

Latest commit

 

History

History
155 lines (124 loc) · 5.27 KB

AGENT_HANDOFFS_IMPLEMENTATION.md

File metadata and controls

155 lines (124 loc) · 5.27 KB

Agent Handoffs System - Implementation Complete

Overview

The agent handoffs system for multi-agent orchestration has been successfully implemented. This system enables agents to seamlessly hand off tasks to other specialized agents with full context preservation.

Components Implemented

1. Core Handoff System (src/agentic_brain/agents/handoffs.py)

  • Handoff - Data structure representing a handoff request
  • HandoffResult - Result of handoff execution with success/error info
  • HandoffRegistry - Registry for managing available agents and executing handoffs

2. Runner Integration (src/agentic_brain/agents/runner.py)

  • Updated AgentRunner to support handoff registry
  • Added handoff_results to RunnerResult
  • Enhanced _handle_handoff method to actually execute handoffs

3. Specialized Agents (src/agentic_brain/agents/specialized/)

  • ResearchAgent - For research tasks, information gathering, analysis
  • CodeAgent - For code generation, debugging, optimization
  • ReviewAgent - For code review, quality analysis, security checks

4. Comprehensive Tests (tests/agents/test_handoffs.py)

  • 26 test cases covering all aspects of the handoff system
  • Tests registry operations, handoff execution, error handling, context preservation
  • Tests specialized agent integration and chain of handoffs

Usage Examples

Basic Handoff Registry Setup

from agentic_brain.agents.handoffs import HandoffRegistry
from agentic_brain.agents.specialized import ResearchAgent, CodeAgent, ReviewAgent

# Create registry and register agents
registry = HandoffRegistry()
registry.register("research", ResearchAgent)
registry.register("code", CodeAgent)
registry.register("review", ReviewAgent)

# Execute a handoff
context = {
    "task": "Research Python async patterns",
    "query": "Python async patterns",
    "domain": "programming"
}

result = await registry.handoff("research", context)
if result.success:
    print(result.agent_result.output)

Runner with Handoff Support

from agentic_brain.agents.runner import AgentRunner
from agentic_brain.agents.handoffs import HandoffRegistry

# Create runner with handoff capability
registry = HandoffRegistry()
registry.register("research", ResearchAgent)

runner = AgentRunner(
    name="orchestrator",
    handoff_registry=registry
)

# Runner can now handle NextStep.HANDOFF

Chain of Handoffs Example

# 1. Research task
research_result = await registry.handoff("research", {
    "task": "Research best coding practices",
    "query": "Python coding best practices",
    "domain": "programming"
})

# 2. Generate code based on research
code_result = await registry.handoff("code", {
    "task": "Generate code following best practices",
    "language": "python",
    "research_findings": research_result.agent_result.output
})

# 3. Review the generated code
review_result = await registry.handoff("review", {
    "task": "Review generated code",
    "code": "# Generated code here",
    "language": "python",
    "focus_areas": ["quality", "best_practices"]
})

Key Features

✅ Context Preservation

  • Full context is passed between agents
  • Previous context is preserved in previous_context field
  • Handoff reason and metadata are maintained

✅ Error Handling

  • Comprehensive error handling for missing agents
  • Agent execution failures are caught and reported
  • Graceful degradation when handoff registry is not available

✅ Flexible Agent Registration

  • Support for class-based and factory function registration
  • Agent instances are cached for efficiency
  • Easy registration/unregistration of agents

✅ Rich Metadata

  • Detailed execution timing and performance metrics
  • Handoff history tracking
  • Agent-specific metadata preservation

✅ Integration with Existing Systems

  • Seamless integration with existing NextStep control flow
  • Compatible with current AgentRunner architecture
  • No breaking changes to existing code

Test Results

================================================= 26 passed in 15.74s ==================================================

All 26 tests pass, covering:

  • Handoff data structures ✅
  • Registry operations ✅
  • Specialized agent execution ✅
  • Runner integration ✅
  • Error handling ✅
  • Context preservation ✅
  • Chain of handoffs ✅

Files Created/Modified

New Files:

  • src/agentic_brain/agents/handoffs.py - Core handoff system
  • src/agentic_brain/agents/specialized/__init__.py - Specialized agents package
  • src/agentic_brain/agents/specialized/research_agent.py - Research agent
  • src/agentic_brain/agents/specialized/code_agent.py - Code agent
  • src/agentic_brain/agents/specialized/review_agent.py - Review agent
  • tests/agents/test_handoffs.py - Comprehensive test suite

Modified Files:

  • src/agentic_brain/agents/runner.py - Added handoff registry support

Next Steps

  1. Deploy and Test: The system is ready for production use
  2. Add More Specialized Agents: Create domain-specific agents as needed
  3. Monitoring: Add metrics and observability for handoff performance
  4. Advanced Features: Consider adding handoff queuing, load balancing, etc.

The agent handoffs system is now fully implemented and tested, enabling seamless multi-agent orchestration with proper context preservation and error handling.