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layout default
title Chapter 6: AgentOS Runtime and Control Plane
nav_order 6
parent Agno Tutorial

Chapter 6: AgentOS Runtime and Control Plane

Welcome to Chapter 6: AgentOS Runtime and Control Plane. In this part of Agno Tutorial: Multi-Agent Systems That Learn Over Time, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.

AgentOS provides runtime and control-plane support for operating Agno systems in production.

Runtime Concerns

Concern Practice
deployment topology isolate workloads by environment and risk
execution state durable storage and recovery strategy
control-plane access strict auth and role boundaries

Control Plane Playbook

  1. define service ownership and SLOs
  2. expose key runtime metrics and traces
  3. establish rollback and emergency stop procedures

Source References

Summary

You now have an operational model for running Agno via AgentOS infrastructure.

Next: Chapter 7: Guardrails, Evals, and Observability

Depth Expansion Playbook

Source Code Walkthrough

cookbook/91_tools/trafilatura_tools.py

The performance_optimized function in cookbook/91_tools/trafilatura_tools.py handles a key part of this chapter's functionality:

def performance_optimized():
    """
    Optimized configuration for fast, efficient extraction.
    Best for high-volume processing or when speed is critical.
    """
    print("\n=== Example 14: Performance Optimized Extraction ===")

    agent = Agent(
        tools=[
            TrafilaturaTools(
                output_format="txt",
                include_comments=False,
                include_tables=False,
                include_images=False,
                include_formatting=False,
                include_links=False,
                with_metadata=False,
                favor_precision=True,  # Faster processing
                deduplicate=False,  # Skip deduplication for speed
            )
        ],
        markdown=True,
    )

    agent.print_response(
        "Quickly extract just the main text content from https://news.ycombinator.com optimized for speed"
    )


# =============================================================================

This function is important because it defines how Agno Tutorial: Multi-Agent Systems That Learn Over Time implements the patterns covered in this chapter.

cookbook/91_tools/github_tools.py

The definitions class in cookbook/91_tools/github_tools.py handles a key part of this chapter's functionality:

    # Example: Search code in repository
    # agent.print_response(
    #     "Search for 'Agent' class definitions in the agno-agi/agno repository",
    #     markdown=True,
    # )

    # Example: Search issues and pull requests
    # agent.print_response(
    #     "Find all issues and PRs mentioning 'bug' in the agno-agi/agno repository",
    #     markdown=True,
    # )

    # Example: Creating a pull request (commented out by default)
    # agent.print_response("Create a pull request from 'feature-branch' to 'main' in agno-agi/agno titled 'New Feature' with description 'Implements the new feature'", markdown=True)

    # Example: Creating a branch (commented out by default)
    # agent.print_response("Create a new branch called 'feature-branch' from the main branch in the agno-agi/agno repository", markdown=True)

    # Example: Setting default branch (commented out by default)
    # agent.print_response("Set the default branch to 'develop' in the agno-agi/agno repository", markdown=True)

    # Example: File creation (commented out by default)
    # agent.print_response("Create a file called 'test.md' with content 'This is a test' in the agno-agi/agno repository", markdown=True)

    # Example: Update file (commented out by default)
    # agent.print_response("Update the README.md file in the agno-agi/agno repository to add a new section about installation", markdown=True)

    # Example: Delete file (commented out by default)
    # agent.print_response("Delete the file test.md from the agno-agi/agno repository", markdown=True)

    # Example: Requesting a review for a pull request (commented out by default)
    # agent.print_response("Request a review from user 'username' for pull request #100 in the agno-agi/agno repository", markdown=True)

This class is important because it defines how Agno Tutorial: Multi-Agent Systems That Learn Over Time implements the patterns covered in this chapter.

cookbook/gemini_3/20_workflow.py

The quality_gate function in cookbook/gemini_3/20_workflow.py handles a key part of this chapter's functionality:

# Custom step functions
# ---------------------------------------------------------------------------
def quality_gate(step_input: StepInput) -> StepOutput:
    """Check that the analysis has enough substance to proceed."""
    content = str(step_input.previous_step_content or "")
    if len(content) < 200:
        return StepOutput(
            content="Quality gate failed: analysis too short. Stopping pipeline.",
            stop=True,
            success=False,
        )
    return StepOutput(
        content=content,
        success=True,
    )


def needs_fact_check(step_input: StepInput) -> bool:
    """Decide whether the report needs fact-checking."""
    content = str(step_input.previous_step_content or "").lower()
    indicators = [
        "study",
        "research",
        "percent",
        "%",
        "million",
        "billion",
        "according",
    ]
    return any(indicator in content for indicator in indicators)

This function is important because it defines how Agno Tutorial: Multi-Agent Systems That Learn Over Time implements the patterns covered in this chapter.

cookbook/gemini_3/20_workflow.py

The needs_fact_check function in cookbook/gemini_3/20_workflow.py handles a key part of this chapter's functionality:

def needs_fact_check(step_input: StepInput) -> bool:
    """Decide whether the report needs fact-checking."""
    content = str(step_input.previous_step_content or "").lower()
    indicators = [
        "study",
        "research",
        "percent",
        "%",
        "million",
        "billion",
        "according",
    ]
    return any(indicator in content for indicator in indicators)


# ---------------------------------------------------------------------------
# Build Workflow
# ---------------------------------------------------------------------------
research_pipeline = Workflow(
    id="gemini-research-pipeline",
    name="Research Pipeline",
    description="Research-to-publication pipeline: parallel research, analysis, quality gate, writing, and conditional fact-checking.",
    db=gemini_agents_db,
    steps=[
        # Step 1: Research in parallel (two agents search simultaneously)
        Parallel(
            "Research",
            Step(name="web_research", agent=web_researcher),
            Step(name="deep_research", agent=deep_researcher),
        ),

This function is important because it defines how Agno Tutorial: Multi-Agent Systems That Learn Over Time implements the patterns covered in this chapter.

How These Components Connect

flowchart TD
    A[performance_optimized]
    B[definitions]
    C[quality_gate]
    D[needs_fact_check]
    E[AnalysisRequest]
    A --> B
    B --> C
    C --> D
    D --> E
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