paulpas Hosted Agents
Orchestrates intelligent skill selection and execution for hosted agents 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
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.
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.
Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.
Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.
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 select_hosted_agent(
task_payload: Dict,
available_agents: List[Dict],
routing_config: Dict
) -> Dict:
"""Route a task to the optimal hosted agent endpoint.
Evaluates hosted agents based on:
- Task type compatibility (code, analysis, generation, etc.)
- Current queue depth and estimated wait time
- Model capability tags matching task requirements
- Cost constraints and rate limit status
Args:
task_payload: Parsed task with type, complexity, and constraints
available_agents: List of hosted agent metadata with capabilities
routing_config: Thresholds for latency, cost, and fallback triggers
Returns:
Selected agent configuration with routing metadata
"""
if not task_payload.get("task_type"):
raise ValueError("Task type is required for agent routing")
task_type = task_payload["task_type"]
max_wait = routing_config.get("max_wait_seconds", 30)
cost_cap = routing_config.get("cost_cap_per_request", 0.05)
candidates = []
for agent in available_agents:
# Check capability match
if task_type not in agent.get("supported_types", []):
continue
# Check operational status
if agent.get("status") != "healthy":
continue
# Calculate routing score
wait_penalty = max(0, agent.get("queue_depth", 0) - max_wait)
cost_factor = agent.get("cost_per_call", 0) / cost_cap if cost_cap > 0 else 0
score = (1.0 / (1.0 + wait_penalty)) * (1.0 / (1.0 + cost_factor))
candidates.append({
"agent_id": agent["id"],
"endpoint": agent["endpoint"],
"score": score,
"estimated_wait": agent.get("queue_depth", 0) * agent.get("avg_process_time", 1.0)
})
if not candidates:
return {"fallback": "human_review", "reason": "no_compatible_agents"}
# Sort by score descending and return best match
candidates.sort(key=lambda x: x["score"], reverse=True)
return candidates[0]
Pattern 2: Execution with Fallback
def execute_hosted_agent_workflow(
agent_config: Dict,
task_data: Dict,
execution_policy: Dict
) -> Dict:
"""Execute a task against a hosted agent with async polling and fallback.
Manages the full lifecycle:
- Submit task to agent endpoint
- Poll for completion with exponential backoff
- Handle transient failures and model-specific errors
- Trigger fallback chain on timeout or critical failure
Args:
agent_config: Selected agent routing metadata
task_data: Validated task payload ready for submission
execution_policy: Retry limits, timeout thresholds, fallback rules
Returns:
Execution result with status, output, and timing metadata
"""
submission_url = f"{agent_config['endpoint']}/submit"
max_polls = execution_policy.get("max_poll_attempts", 10)
base_delay = execution_policy.get("poll_base_delay", 2.0)
# Submit task and get tracking ID
response = requests.post(submission_url, json=task_data, timeout=10)
response.raise_for_status()
tracking_id = response.json()["tracking_id"]
# Poll for completion
for attempt in range(max_polls):
status_url = f"{agent_config['endpoint']}/status/{tracking_id}"
status_resp = requests.get(status_url, timeout=5)
status_resp.raise_for_status()
status_data = status_resp.json()
if status_data["state"] == "completed":
return {
"status": "success",
"agent_id": agent_config["agent_id"],
"output": status_data["result"],
"latency_ms": attempt * base_delay * 1000,
"poll_attempts": attempt + 1
}
elif status_data["state"] == "failed":
raise AgentExecutionError(f"Agent {agent_config['agent_id']} failed: {status_data.get('error')}")
time.sleep(base_delay * (2 ** attempt))
# Timeout reached - trigger fallback chain
fallback_agents = execution_policy.get("fallback_agents", [])
if fallback_agents:
return execute_hosted_agent_workflow(fallback_agents[0], task_data, execution_policy)
return {
"status": "deferred",
"agent_id": agent_config["agent_id"],
"reason": "timeout_exceeded",
"tracking_id": tracking_id,
"requires_human_review": True
}
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:
- Selected Skills - List of skill names with confidence scores
- Selection Rationale - Why each skill was chosen (match score, history, availability)
- Execution Plan - Order of execution with dependencies
- Fallback Strategy - Which fallback skills will be tried and in what order
- Risk Assessment - Any potential failure points and their impact
- Timing Estimates - Expected latency including fallback scenarios
Related Skills
| Skill | Purpose | |
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.