agent-ops

name

agent-ops

description

Enterprise SRE patterns for AI agent operations. Provides cost caps, circuit breakers, stall detection, observability, and runbook-driven incident response for autonomous agent workflows. Use when: (1) Running long autonomous agent sessions, (2) Managing multi-agent swarms, (3) Monitoring agent costs and performance, (4) Debugging stuck or expensive agent loops, (5) Setting up agent observability.

Agent Ops — SRE for AI Agents

Quick Reference

Pattern	Purpose
Cost Cap	Hard-stop agent when spend exceeds budget
Circuit Breaker	Halt after N consecutive failures
Stall Detector	Kill agent stuck in loops
Health Dashboard	Real-time agent metrics
Runbook	Automated incident response

Cost Cap Pattern

Set maximum spend per session or per agent:

# Check current session cost
METRICS_FILE="$HOME/{{TOOL_DIR}}/metrics/costs.jsonl"
if [[ -f "$METRICS_FILE" ]]; then
    SESSION_ID="${CLAUDE_SESSION_ID:-default}"
    TOTAL=$(grep "$SESSION_ID" "$METRICS_FILE" | \
        python3 -c "import sys,json; print(sum(json.loads(l)['estimated_cost_usd'] for l in sys.stdin))")
    echo "Session cost: \$$TOTAL"
fi

Implementation in agent workflows:

# Cost cap check — add to any long-running agent loop
MAX_COST_USD = float(os.environ.get("AGENT_COST_CAP", "5.00"))

def check_cost_cap(session_id: str) -> bool:
    """Return True if within budget, False if exceeded."""
    metrics_file = Path.home() / ".claude" / "metrics" / "costs.jsonl"
    if not metrics_file.exists():
        return True

    total = 0.0
    for line in metrics_file.read_text().splitlines():
        try:
            row = json.loads(line)
            if row.get("session_id") == session_id:
                total += row.get("estimated_cost_usd", 0)
        except json.JSONDecodeError:
            continue

    return total < MAX_COST_USD

Environment variables:

Variable	Default	Description
AGENT_COST_CAP	5.00	Max USD per session
AGENT_COST_CAP_WARN	0.80	Warning threshold (fraction of cap)
AGENT_COST_ACTION	stop	Action on cap: stop, warn, log

Circuit Breaker Pattern

Stop after N consecutive failures to prevent cost waste:

class CircuitBreaker:
    """Stop agent after consecutive failures."""

    def __init__(self, max_failures: int = 3, reset_after_success: bool = True):
        self.max_failures = max_failures
        self.reset_after_success = reset_after_success
        self.consecutive_failures = 0
        self.state = "closed"  # closed=healthy, open=tripped, half-open=testing

    def record_success(self):
        if self.reset_after_success:
            self.consecutive_failures = 0
        self.state = "closed"

    def record_failure(self, error: str = ""):
        self.consecutive_failures += 1
        if self.consecutive_failures >= self.max_failures:
            self.state = "open"
            raise CircuitBreakerTripped(
                f"Circuit breaker tripped after {self.max_failures} consecutive failures. "
                f"Last error: {error}"
            )

    def allow_request(self) -> bool:
        return self.state != "open"

When to use:

• API calls to external services
• Build/test commands that may fail repeatedly
• Agent tool use in autonomous loops

Stall Detector Pattern

Detect and kill agents stuck in repetitive loops:

class StallDetector:
    """Detect agent stuck in loops by tracking action hashes."""

    def __init__(self, window_size: int = 10, similarity_threshold: float = 0.8):
        self.window_size = window_size
        self.similarity_threshold = similarity_threshold
        self.action_history: list[str] = []

    def record_action(self, action_summary: str):
        self.action_history.append(action_summary)
        if len(self.action_history) > self.window_size * 2:
            self.action_history = self.action_history[-self.window_size * 2:]

    def is_stalled(self) -> bool:
        if len(self.action_history) < self.window_size:
            return False

        recent = self.action_history[-self.window_size:]
        unique_ratio = len(set(recent)) / len(recent)

        # If most recent actions are repetitive, we're stalled
        return unique_ratio < (1 - self.similarity_threshold)

Indicators of stall:

• Same file read/written > 3 times in a row
• Same error message appearing repeatedly
• Agent alternating between 2-3 actions without progress
• Token usage climbing without task completion

Health Dashboard

Quick command to check agent health:

#!/usr/bin/env bash
# agent-health.sh — Quick health check for running agents

echo "=== Agent Health Dashboard ==="
echo ""

# Active tmux sessions (agents)
echo "## Active Agent Sessions"
tmux list-sessions 2>/dev/null | grep -E "^(dev-|agent-|swarm-)" || echo "  No active sessions"
echo ""

# Cost metrics
echo "## Cost Metrics (last 24h)"
METRICS="$HOME/{{TOOL_DIR}}/metrics/costs.jsonl"
if [[ -f "$METRICS" ]]; then
    CUTOFF=$(date -u -v-24H +%Y-%m-%dT%H:%M:%S 2>/dev/null || date -u -d '24 hours ago' +%Y-%m-%dT%H:%M:%S)
    python3 -c "
import json, sys
total = 0
count = 0
for line in open('$METRICS'):
    try:
        r = json.loads(line)
        if r['timestamp'] >= '$CUTOFF':
            total += r.get('estimated_cost_usd', 0)
            count += 1
    except: pass
print(f'  Sessions: {count}')
print(f'  Total cost: \${total:.4f}')
print(f'  Avg cost/session: \${total/max(count,1):.4f}')
"
else
    echo "  No metrics file found"
fi
echo ""

# Disk usage
echo "## Knowledge Base Size"
du -sh "$HOME/.learnings/" 2>/dev/null || echo "  No knowledge base"
du -sh "$HOME/{{TOOL_DIR}}/metrics/" 2>/dev/null || echo "  No metrics"

Runbook: Common Agent Incidents

Agent Stuck in Loop

1. Check tmux session: tmux capture-pane -t <session> -p -S -50
2. If stalled: tmux kill-session -t <session>
3. Review last actions in transcript
4. Restart with more specific instructions

Cost Spike

1. Check {{HOME_TOOL_DIR}}/metrics/costs.jsonl for recent sessions
2. Identify high-cost session IDs
3. Set cost cap: export AGENT_COST_CAP=2.00
4. Consider using haiku for sub-tasks: model parameter in Agent tool

Agent Producing Wrong Output

1. Check if agent has correct context (CLAUDE.md loaded?)
2. Review agent type — is it specialized enough?
3. Add constraints to agent prompt
4. Consider /reflect to capture the correction

Swarm Coordination Failure

1. Check team config: cat {{HOME_TOOL_DIR}}/teams/*/config.json
2. Verify task list: use TaskList tool
3. Check for blocked tasks with unresolved dependencies
4. Send broadcast to all agents if needed

Observability Integration

Structured Logging

All agent operations should log structured events:

import json
from datetime import datetime, timezone
from pathlib import Path

def log_agent_event(event_type: str, data: dict, agent_id: str = "main"):
    """Append structured event to agent log."""
    log_dir = Path.home() / ".claude" / "metrics" / "events"
    log_dir.mkdir(parents=True, exist_ok=True)

    event = {
        "timestamp": datetime.now(timezone.utc).isoformat(),
        "agent_id": agent_id,
        "event_type": event_type,
        **data,
    }

    log_file = log_dir / f"{datetime.now().strftime('%Y-%m-%d')}.jsonl"
    with open(log_file, "a") as f:
        f.write(json.dumps(event) + "\n")

Event Types

Event	Fields	When
agent.start	model, task_summary	Agent spawned
agent.complete	duration_ms, tokens_used	Agent finished
agent.error	error_type, message	Agent failed
agent.cost	input_tokens, output_tokens, cost_usd	Per-turn cost
circuit.trip	failures, last_error	Circuit breaker tripped
stall.detect	action_count, unique_ratio	Stall detected
cost.warn	current_cost, cap	Approaching cost cap
cost.exceed	current_cost, cap	Cost cap exceeded