Core Build: Full Orchestration System

Task Decomposition Strategy

The orchestrator must intelligently split complex tasks into agent-specific subtasks.

Decomposition Patterns:

By Task Type:

• Analysis tasks → Research agents
• Writing tasks → Content agents
• Data tasks → Processing agents

By Data Scope:

• Split large datasets into chunks
• Assign chunks to parallel agents
• Merge results at the end

By Priority:

• Critical path tasks execute first
• Non-blocking tasks run in parallel
• Dependencies determine order

Example decomposition logic:

tasks = [Task(type="research", data=chunk, priority=1)
         for chunk in split_data(input_data)]
agent_map = {"research": ResearchAgent, "write": WriterAgent}

Agent Registry and Discovery

The orchestrator maintains a registry of available agents and their capabilities.

Registry Structure:

Agent Metadata:

• Agent ID and type
• Capabilities (tasks it can handle)
• Current status (idle, busy, failed)
• Resource requirements

Discovery Process:

• Register agents on startup
• Query capabilities for task matching
• Load balance across available agents
• Handle agent failures gracefully

Example registry implementation:

registry = {agent.id: {"type": agent.type, "status": "idle"}
            for agent in available_agents}

Task Queue Management

Efficient task queuing ensures optimal resource utilization.

Queue Mechanics:

Priority Queue:

• High priority tasks execute first
• FIFO within same priority level
• Dynamic priority adjustment

Task States:

• Pending: In queue waiting
• Running: Currently executing
• Completed: Finished successfully
• Failed: Encountered error

Queue Operations:

queue.put((priority, timestamp, task))
task = await queue.get()  # Blocks until task available
queue.task_done()

Result Aggregation Patterns

Collecting and synthesizing results from multiple agents requires careful design.

Aggregation Strategies:

Merge Pattern:

• Combine similar outputs
• Remove duplicates
• Sort by relevance

Reduce Pattern:

• Fold results into single output
• Apply transformations
• Maintain consistency

Example aggregation:

final = reduce(lambda a,b: merge(a,b), results)
return {"status": "complete", "data": final}

Concurrent Agent Execution with Asyncio

Python's asyncio enables efficient parallel execution for I/O-bound agent tasks.

Asyncio Fundamentals:

Event Loop:

• Single thread manages multiple coroutines
• Switches between tasks at await points
• Ideal for network calls and API requests

Coroutines:

• Functions defined with async def
• Use await to yield control
• Run concurrently, not in parallel

Example concurrent execution:

results = await asyncio.gather(
    agent1.process(task1), agent2.process(task2))

Gathers results from multiple agents running concurrently.

Thread Pool vs Async/Await Patterns

Choose the right concurrency model based on task characteristics.

Async/Await (I/O-bound):

• Best for: API calls, file I/O, database queries
• Single thread, event-driven
• Low overhead, high throughput
• Example: Web scraping, data fetching

Thread Pool (CPU-bound):

• Best for: Data processing, calculations, transformations
• True parallelism on multi-core CPUs
• Higher overhead, limited by GIL
• Example: Image processing, statistical analysis

Hybrid Approach:

loop = asyncio.get_event_loop()
cpu_result = await loop.run_in_executor(pool, cpu_task)

Run CPU-bound tasks in thread pool while maintaining async interface.

Synchronization Points

Coordinate agent execution at critical workflow stages.

Synchronization Patterns:

Barrier Pattern:

• All agents must reach checkpoint
• Wait for slowest agent
• Proceed together after sync

Semaphore Pattern:

• Limit concurrent resource access
• Control rate of execution
• Prevent resource exhaustion

Lock Pattern:

• Ensure exclusive access to shared state
• Prevent race conditions
• Use sparingly to avoid deadlocks

Example synchronization:

async with semaphore:  # Limit to N concurrent agents
    result = await agent.process(task)

Error Handling in Parallel Workflows

Robust error handling prevents cascade failures in parallel execution.

Error Strategies:

Retry with Backoff:

• Retry failed tasks automatically
• Exponential backoff prevents overload
• Maximum retry limit

Fallback Agents:

• Switch to backup agent on failure
• Maintain workflow continuity
• Log original failure

Partial Success:

• Continue with available results
• Mark failed tasks explicitly
• Allow graceful degradation

Example error handling:

try:
    result = await agent.process(task)
except AgentError:
    result = await fallback_agent.process(task)

Start/Stop/Restart Workflow

Implement lifecycle management for orchestrated workflows.

Workflow States:

Start:

• Initialize agent registry
• Load task queue
• Begin execution loop
• Return workflow ID

Stop:

• Cancel pending tasks
• Wait for running tasks to complete
• Save state for resume
• Release resources

Restart:

• Load saved state
• Re-register agents
• Resume from checkpoint
• Handle failed tasks

Example workflow control:

workflow.start()  # Begin execution
workflow.stop()   # Graceful shutdown
workflow.restart(checkpoint_id)  # Resume

Pause and Resume

Support workflow suspension for maintenance or resource constraints.

Pause Mechanism:

Checkpoint Creation:

• Save current task queue
• Record agent states
• Persist partial results
• Generate resume token

Resume Process:

• Load checkpoint data
• Restore agent states
• Continue from last task
• Validate consistency

Use Cases:

• System maintenance windows
• Resource quota management
• User-initiated suspension
• Error investigation

Example pause/resume:

checkpoint = workflow.pause()
# Later...
workflow.resume(checkpoint)

Cancel Individual Tasks

Allow granular task cancellation without disrupting the entire workflow.

Cancellation Process:

Task Selection:

• Cancel by task ID
• Cancel by agent ID
• Cancel by priority range
• Cancel by task type

Cleanup:

• Stop task execution immediately
• Release allocated resources
• Remove from queue
• Update status to cancelled

Dependencies:

• Check for dependent tasks
• Cancel dependents or reassign
• Maintain workflow integrity

Example cancellation:

workflow.cancel_task(task_id)
# Returns: {"status": "cancelled", "dependents": []}

Monitor Progress

Real-time monitoring provides visibility into workflow execution.

Monitoring Metrics:

Task Metrics:

• Total tasks: pending, running, completed, failed
• Average execution time
• Throughput (tasks per second)
• Error rate

Agent Metrics:

• Active agents count
• Idle vs busy distribution
• Agent failure rate
• Resource utilization

Workflow Metrics:

• Overall progress percentage
• Estimated completion time
• Bottleneck identification
• Critical path status

Example progress query:

status = workflow.get_status()
# {"progress": 65, "active": 8, "completed": 130}

Collect Results from Multiple Agents

Gather outputs from parallel agents efficiently.

Collection Strategies:

Streaming Collection:

• Collect results as they arrive
• Process incrementally
• Reduce memory footprint
• Enable early termination

Batch Collection:

• Wait for all agents to complete
• Collect results together
• Simplify synchronization
• Higher latency

Hybrid Collection:

• Process available results immediately
• Wait for critical results
• Balance throughput and latency

Example result collection:

async for result in workflow.results_stream():
    process_result(result)  # Handle as available

Handle Partial Failures

Gracefully manage scenarios where some agents fail.

Failure Handling:

Mark Failed Tasks:

• Tag results with success/failure status
• Include error messages
• Preserve partial outputs
• Enable retry decision

Threshold-Based Decisions:

• Proceed if N% of tasks succeed
• Fail workflow if critical tasks fail
• Retry failed tasks automatically
• Escalate to human if threshold not met

Fallback Values:

• Use default values for failed tasks
• Interpolate from successful results
• Mark missing data explicitly

Example partial failure handling:

if success_rate > 0.8:
    return merge_results(successful_results)
else:
    retry_failed_tasks()

Synthesize Final Output

Combine individual agent outputs into coherent final result.

Synthesis Strategies:

Merge Strategy:

• Combine similar outputs (e.g., concatenate text)
• Remove duplicates
• Sort by relevance or timestamp

Reduce Strategy:

• Apply reduction function across results
• Example: sum, average, max, min
• Preserve metadata

Transform Strategy:

• Convert results to target format
• Apply post-processing rules
• Add computed fields

Example synthesis:

final = {"summary": merge([r.text for r in results]),
         "confidence": avg([r.score for r in results])}

Return Structured Results

Provide well-formatted output with metadata.

Result Structure:

Core Fields:

• status: "success" | "partial" | "failed"
• data: Aggregated output
• metadata: Execution statistics
• errors: List of failures if any

Metadata Fields:

• Execution time
• Number of agents used
• Success rate
• Resource consumption

Error Information:

• Failed task IDs
• Error messages
• Suggested remediation
• Retry recommendations

Example structured result:

return {"status": "success", "data": final_output,
        "metadata": {"time": elapsed, "agents": 12}}