---

name: hosted-agents compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent hosted agents with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense license: MIT maturity: stable metadata: domain: agent output-format: analysis related-skills: agent-confidence-based-selector, agent-task-routing role: orchestration scope: orchestration triggers: hosted-agents, hosted agents, how do i hosted-agents, orchestrate hosted-agents, automate hosted-agents, agent hosted-agents archetypes:
  • orchestration
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • single-agent monolith response_profile: verbosity: medium directive_strength: high abstraction_level: tactical version: "1.0.0"

---

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

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 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:

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

| Skill | Purpose | |

---

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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.

• OpenAI Agents SDK Documentation
• Anthropic Claude API for Agents
• Google Gemini for Agent Development
• Agent Hosting Platforms Comparison
• LLM API Cost Optimization Guide