Execution Flow Architecture

How user requests flow through an OSSA agent from start to finish.

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Overview

This page visualizes the complete lifecycle of a user request through an OSSA-defined agent, showing:

• User interaction points
• Agent orchestration steps
• LLM processing
• Tool execution
• Response generation

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Complete Execution Flow

sequenceDiagram
    autonumber
    participant User
    participant App as Application Layer
    participant Runtime as Agent Runtime
    participant Orchestrator as Agent Orchestrator
    participant Manifest as OSSA Manifest
    participant LLM as LLM Provider
    participant Tools as Tool Registry
    participant API as External APIs

    User->>App: Send Request
    Note over User,App: "What's the weather in SF?"

    App->>Runtime: Forward Request
    Runtime->>Orchestrator: Initialize Agent

    Orchestrator->>Manifest: Load OSSA Manifest
    Note over Manifest: apiVersion: ossa/v0.3.x<br/>kind: Agent<br/>spec: {...}

    Manifest-->>Orchestrator: Agent Config
    Note over Orchestrator: Role, Model, Tools, Rules

    Orchestrator->>LLM: Send Request + Context
    Note over LLM: User message<br/>+ System prompt (role)<br/>+ Available tools

    LLM-->>Orchestrator: Tool Call Request
    Note over LLM: Function: get_weather<br/>Args: {city: "SF"}

    Orchestrator->>Tools: Lookup Tool
    Tools-->>Orchestrator: Tool Definition

    Orchestrator->>API: Execute Tool
    Note over API: GET /weather?city=SF

    API-->>Orchestrator: Tool Result
    Note over API: {temp: 68°F, sunny}

    Orchestrator->>LLM: Send Tool Result
    LLM-->>Orchestrator: Final Response
    Note over LLM: "It's 68°F and sunny in SF"

    Orchestrator->>Runtime: Format Response
    Runtime->>App: Return Result
    App->>User: Display Response
    Note over User,App: "It's 68°F and sunny<br/>in San Francisco"

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Step-by-Step Breakdown

Step 1-3: User Request Handling

User → Application Layer → Agent Runtime

What happens:

• User sends a natural language request
• Application layer receives and validates the request
• Request is forwarded to the agent runtime for processing

OSSA's role: None yet - this is application infrastructure

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Step 4-6: Agent Initialization

Agent Runtime → Orchestrator → OSSA Manifest

What happens:

• Runtime initializes the agent orchestrator
• Orchestrator loads the OSSA manifest (YAML/JSON)
• Manifest is parsed and validated against the schema

OSSA's role:

• Defines agent configuration via manifest
• Provides schema validation
• Specifies role, model, tools, and constraints

Example Manifest:

apiVersion: ossa/v0.3.x
kind: Agent
metadata:
  name: weather-assistant
spec:
  role: You are a helpful weather assistant
  llm:
    provider: openai
    model: gpt-3.5-turbo
  tools:
    - type: function
      name: get_weather
      description: Get current weather for a city

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Step 7-8: LLM Processing

Orchestrator → LLM Provider

What happens:

• Orchestrator sends the user message to the LLM
• Includes system prompt from OSSA manifest (spec.role)
• Includes available tools from OSSA manifest (spec.tools)
• LLM processes and determines if tools are needed

OSSA's role:

• Defines which LLM provider to use (spec.llm.provider)
• Defines which model to use (spec.llm.model)
• Defines system prompt (spec.role)
• Defines available tools (spec.tools[])

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Step 9-12: Tool Execution

LLM → Orchestrator → Tool Registry → External API

What happens:

• LLM requests tool execution (e.g., get_weather)
• Orchestrator looks up tool definition
• Tool is executed against external API
• Result is returned to orchestrator

OSSA's role:

• Defines tool schema in manifest
• Specifies tool execution constraints
• Defines error handling rules

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Step 13-14: Response Generation

Orchestrator → LLM → Final Response

What happens:

• Tool result is sent back to LLM
• LLM generates natural language response
• Response includes tool data formatted for user

OSSA's role:

• Defines response formatting rules
• Specifies output constraints
• Controls token limits and timeouts

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Step 15-17: Response Delivery

LLM → Runtime → Application → User

What happens:

• Final response flows back through the stack
• Runtime formats response per OSSA spec
• Application layer delivers to user

OSSA's role:

• Defines response structure
• Specifies metadata to include
• Controls observability data

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Alternative Flow: No Tools Needed

sequenceDiagram
    autonumber
    participant User
    participant Runtime as Agent Runtime
    participant Orchestrator as Agent Orchestrator
    participant LLM as LLM Provider

    User->>Runtime: "Tell me a joke"
    Runtime->>Orchestrator: Process Request
    Orchestrator->>LLM: Send Request + Role
    Note over LLM: No tools needed
    LLM-->>Orchestrator: Direct Response
    Note over LLM: "Why did the chicken<br/>cross the road?..."
    Orchestrator->>Runtime: Format Response
    Runtime->>User: Display Joke

What's different:

• LLM responds directly without tool execution
• Faster response time
• Simpler execution path

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Error Handling Flow

sequenceDiagram
    autonumber
    participant Orchestrator as Agent Orchestrator
    participant LLM as LLM Provider
    participant Tool as External Tool
    participant ErrorHandler as Error Handler

    Orchestrator->>LLM: Process Request
    LLM->>Orchestrator: Tool Call
    Orchestrator->>Tool: Execute Tool
    Tool-->>Orchestrator: ❌ Error (API Down)

    Orchestrator->>ErrorHandler: Handle Error
    Note over ErrorHandler: Check OSSA manifest<br/>error handling rules

    ErrorHandler->>Orchestrator: Retry Strategy
    Orchestrator->>Tool: Retry Execution

    alt Success
        Tool-->>Orchestrator: ✅ Result
        Orchestrator->>LLM: Continue
    else Failure
        Tool-->>Orchestrator: ❌ Still Failing
        ErrorHandler->>LLM: Fallback Response
        Note over LLM: "I'm having trouble<br/>accessing that data"
    end

OSSA's role in error handling:

• Defines retry policies (spec.tools[].retry)
• Specifies timeout limits (spec.tools[].timeout)
• Controls fallback behavior (spec.errorHandling)

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Key Takeaways

What OSSA Defines

• ✅ Agent configuration (role, model, tools)
• ✅ LLM provider and model selection
• ✅ Tool schemas and constraints
• ✅ Error handling policies
• ✅ Response formatting rules

What OSSA Does NOT Define

• ❌ Application layer routing
• ❌ Runtime implementation details
• ❌ LLM API communication protocols
• ❌ External tool implementations
• ❌ User interface rendering

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Execution Flow Properties

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Performance Considerations

Latency Breakdown

gantt
    title Typical Request Latency
    dateFormat X
    axisFormat %Ls

    section App Layer
    Request Validation    :0, 50

    section Agent Init
    Load Manifest        :50, 100
    Parse & Validate     :100, 150

    section LLM Call
    Network Latency      :150, 300
    LLM Processing       :300, 1500

    section Tool Execution
    Tool Lookup          :1500, 1550
    API Call             :1550, 2050

    section Response
    Format Response      :2050, 2100
    Return to User       :2100, 2150

Optimization tips:

• Cache parsed OSSA manifests (Steps 5-6)
• Use streaming for LLM responses (Step 8)
• Parallelize tool calls when possible (Steps 10-12)
• Implement response caching (Step 14)

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Related Documentation

• Stack Integration - Where OSSA fits in your architecture
• Schema Reference - Detailed tool execution patterns
• Specification - Full OSSA spec details
• Ecosystem Overview - Framework integrations

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Next: Stack Integration Diagram - See where OSSA fits in your technology stack