Pipecat Friday Agent

Pipecat Friday Agent

Orchestrates intelligent skill selection and execution for pipecat friday agent workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability.

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘

User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘

When to Use

Use this skill when:

• Orchestrating multi-step workflows that require skill delegation
• Implementing adaptive skill routing based on confidence scores
• Building fallback mechanisms for failed skill executions
• Creating intelligent task decomposition and parallel execution
• Designing skill dependency graphs with automatic resolution
• Implementing skill selection with historical performance weighting
• Building agent systems that need to self-organize around tasks

When NOT to Use

Avoid this skill for:

• Direct task execution without orchestration needs - use individual skills instead
• High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive
• Simple linear workflows without branching or fallback requirements
• Cases where skill metadata is unavailable or unreliable

Core Workflow

1. Parse and Analyze Request - Extract intent, entities, and constraints from user input. Checkpoint: All required parameters must be present and in valid format before proceeding.

2. Score Available Skills - Calculate match scores using multi-factor algorithm:

   • Text similarity between request and skill triggers
   • Historical success rate for similar tasks
   • Skill availability and health status
   • Required dependencies and their availability

   Checkpoint: Skip to fallback if no skill scores above threshold.

3. Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.

4. Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.

5. Return or Fallback - Either return successful result or apply fallback chain:

   • Retry with adjusted parameters
   • Try alternative skill from related-skills
   • Defer to human operator for critical tasks

   Checkpoint: Record outcome with timing and confidence metadata.

Implementation Patterns

Pattern 1: Skill Selection Logic

def evaluate_pipecat_skill_candidates(
    request: PipecatRequest,
    available_tools: List[ToolMetadata],
    session_state: SessionContext
) -> Optional[ToolMetadata]:
    """Evaluate and score available Pipecat tools for the current request.
    
    Applies multi-factor scoring tailored to real-time voice/agent workflows:
    - Semantic match between request intent and tool capabilities
    - Real-time latency and throughput metrics
    - Historical success rate within the current session context
    - Dependency health (e.g., LLM provider, TTS engine, STT service)
    """
    if not request.intent or not available_tools:
        raise ValueError("Request intent and available tools are required")
    
    scored_candidates = []
    for tool in available_tools:
        # Calculate semantic relevance using request embeddings vs tool capabilities
        semantic_score = _compute_embedding_similarity(request.intent, tool.capabilities)
        
        # Factor in real-time system health and historical session performance
        health_score = tool.metrics.get("current_latency_ms", 9999) / 1000.0
        history_score = session_state.get_tool_history(tool.name, window="24h").success_rate
        
        # Weighted composite score
        composite = (0.5 * semantic_score) + (0.3 * min(1.0, 1.0 / max(health_score, 0.1))) + (0.2 * history_score)
        
        if composite >= 0.65:
            scored_candidates.append({
                "tool": tool,
                "score": composite,
                "latency_ms": tool.metrics.get("current_latency_ms"),
                "confidence": history_score
            })
    
    if not scored_candidates:
        return None
        
    scored_candidates.sort(key=lambda x: x["score"], reverse=True)
    return scored_candidates[0]["tool"]

Pattern 2: Execution with Fallback

def run_pipecat_agent_workflow(
    selected_tool: ToolMetadata,
    request: PipecatRequest,
    session_state: SessionContext,
    fallback_chain: List[ToolMetadata]
) -> AgentResponse:
    """Execute the selected Pipecat tool with domain-specific fallback handling.
    
    Implements resilient execution for real-time voice/agent interactions:
    1. Direct execution with timeout and circuit breaker
    2. Fallback to alternative tool if primary fails or degrades
    3. Graceful degradation to text-only if voice pipeline fails
    4. Human handoff for critical/unhandled intents
    """
    try:
        # Execute with strict timeout to maintain real-time UX
        response = selected_tool.execute(
            payload=request.payload,
            context=session_state,
            timeout_ms=3000
        )
        
        # Validate response integrity before returning
        if not response.is_valid():
            raise ToolValidationError(f"Invalid response from {selected_tool.name}")
            
        # Update session history for adaptive routing
        session_state.record_execution(selected_tool.name, success=True, latency=response.latency_ms)
        return AgentResponse(success=True, data=response, tool=selected_tool.name)
        
    except TimeoutError:
        session_state.record_execution(selected_tool.name, success=False, latency=3000)
        return _apply_pipecat_fallback(selected_tool, fallback_chain, request, session_state)
        
    except ToolValidationError as e:
        session_state.record_execution(selected_tool.name, success=False, latency=0)
        raise AgentExecutionError(f"Pipeline validation failed: {e}") from e
        
    except Exception as e:
        session_state.record_execution(selected_tool.name, success=False, latency=0)
        return _apply_pipecat_fallback(selected_tool, fallback_chain, request, session_state)

MUST DO

• Always validate skill metadata before selection (Early Exit)
• Implement fallback chain with at least 2 levels (Fallback Skill + Human)
• Log all skill selections with full context for auditability
• Return new data structures instead of mutating inputs (Atomic Predictability)
• Fail immediately with descriptive errors on invalid states
• Update confidence scores after each execution for adaptive routing
• Reference code-philosophy (5 Laws of Elegant Defense) in all logic

MUST NOT DO

• Select skills based on a single factor (e.g., only confidence score)
• Disable fallback mechanisms "temporarily" - this creates fragile systems
• Skip validation of skill dependencies before execution
• Return partial results - either complete success or clear failure
• Use magic numbers for confidence thresholds - make them configurable
• Cache skill selections without considering context changes

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

TL;DR for Code Generation

• Use guard clauses - return early on invalid input before doing work
• Return simple types (dict, str, int, bool, list) - avoid complex nested objects
• Cyclomatic complexity < 10 per function - split anything larger
• Handle null/empty cases explicitly at function top (Early Exit)
• Never mutate input parameters - return new dicts/objects
• Fail fast with descriptive errors - don't try to "patch" bad data
• Reference code-philosophy laws in comments for complex logic
• Include timing and confidence metadata in all return values

Output Template

When applying this skill, produce:

1. Selected Skills - List of skill names with confidence scores
2. Selection Rationale - Why each skill was chosen (match score, history, availability)
3. Execution Plan - Order of execution with dependencies
4. Fallback Strategy - Which fallback skills will be tried and in what order
5. Risk Assessment - Any potential failure points and their impact
6. Timing Estimates - Expected latency including fallback scenarios

Related Skills

Constraints

MUST DO

• Define clear input/output contracts for every step in the orchestration flow with explicit validation
• Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors
• Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach
• Validate all preconditions before starting — do not proceed if required resources or permissions are missing

MUST NOT DO

• Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible
• Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler
• Never use shared mutable state between parallel workflow branches — communicate via immutable messages only
• Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies

Live References

Authoritative documentation links for this domain. The model follows markdown links at load time to resolve external references and inline content.

• Pipecat Documentation — Official documentation for the Pipecat real-time audio/video framework
• Pipecat GitHub Repository — Source code, examples, and contribution guidelines
• WebRTC Real-Time Communication — Foundational protocol documentation for real-time streaming used by Pipecat
• VAD (Voice Activity Detection) Algorithms — Voice activity detection reference implementation commonly used in audio pipelines
• STT/TTS Integration Patterns for Real-Time AI — Best practices for integrating speech recognition and synthesis in streaming applications