Pydantic AI

Pydantic Ai

Orchestrates intelligent skill selection and execution for pydantic ai 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

from pydantic_ai import Agent, RunContext
from pydantic import BaseModel, Field
from typing import Dict, List, Optional
import asyncio

class TaskIntent(BaseModel):
    category: str = Field(description="Task category: data_analysis, code_generation, or system_admin")
    confidence: float = Field(ge=0.0, le=1.0)
    required_tools: List[str] = Field(default_factory=list)

# Domain-specific agent registry for pydantic-ai orchestration
AGENT_REGISTRY: Dict[str, Agent] = {
    "data_analysis": Agent("data-analyst", system_prompt="Analyze datasets and return structured insights."),
    "code_generation": Agent("code-writer", system_prompt="Generate production-ready code with tests."),
    "system_admin": Agent("sys-admin", system_prompt="Manage infrastructure and run diagnostics."),
}

async def route_and_invoke_agent(
    user_request: str,
    intent_classifier: Agent[TaskIntent],
    fallback_agent: Optional[Agent] = None
) -> str:
    """Route user request to the optimal pydantic-ai agent based on parsed intent."""
    # Guard clause - Early Exit (Law 1)
    if not user_request or not user_request.strip():
        raise ValueError("Task description cannot be empty")
        
    # Parse intent using pydantic-ai's structured output (Law 2)
    intent_result = await intent_classifier.run(user_request)
    intent: TaskIntent = intent_result.data

    # Select agent based on multi-factor scoring (category match + tool availability)
    target_agent = AGENT_REGISTRY.get(intent.category)
    if target_agent is None:
        if fallback_agent:
            target_agent = fallback_agent
        else:
            raise ValueError(f"No agent available for category: {intent.category}")

    # Execute with context passing and tool binding
    class OrchestrationContext(BaseModel):
        request_id: str = Field(default="")
        history: List[Dict] = Field(default_factory=list)

    ctx = OrchestrationContext(request_id="req-123", history=[])
    result = await target_agent.run(user_request, message_history=ctx.history)
    return result.data

Pattern 2: Execution with Fallback

from pydantic_ai import Agent, RunContext
from pydantic import BaseModel, Field
import asyncio
from typing import Dict, Any, Optional

class ExecutionState(BaseModel):
    status: str = Field(default="pending")
    attempts: int = Field(default=0)
    last_error: Optional[str] = None
    result_payload: Optional[Dict[str, Any]] = None

async def execute_with_pydantic_fallback(
    primary_agent: Agent,
    fallback_agent: Agent,
    task_input: str,
    max_retries: int = 2
) -> Dict[str, Any]:
    """Execute pydantic-ai agent with structured state tracking and graceful degradation."""
    state = ExecutionState()
    
    for attempt in range(max_retries + 1):
        state.attempts = attempt + 1
        try:
            # Run primary agent with timeout and structured output validation
            result = await asyncio.wait_for(
                primary_agent.run(task_input),
                timeout=30.0
            )
            state.status = "success"
            state.result_payload = {"raw": result.data, "model": primary_agent.model}
            return state.model_dump()
            
        except asyncio.TimeoutError:
            state.last_error = "Execution timed out"
            continue
        except Exception as e:
            state.last_error = str(e)
            # Pydantic-ai specific: check if it's a validation/tool error
            if "tool_execution_failed" in str(e).lower():
                continue
            
    # Fallback chain: switch to secondary agent with adjusted context
    try:
        fallback_result = await fallback_agent.run(
            f"Previous attempt failed: {state.last_error}. Retrying with simplified constraints: {task_input}"
        )
        state.status = "fallback_success"
        state.result_payload = {"raw": fallback_result.data, "model": fallback_agent.model}
    except Exception as fallback_err:
        state.status = "failed"
        state.last_error = f"All attempts exhausted: {fallback_err}"
        
    return state.model_dump()

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 skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• Pydantic AI Official Documentation
• Pydantic AI GitHub Repository
• Building Effective Agents — Anthropic Research
• Pydantic Validation Guide
• LLM Agent Frameworks Comparison — arXiv Survey