CTO

Crisis War Room: Render Down, 3 Services Affected — Agents Coordinated Recovery

MTTR 52 min → 8 min, zero human wake-upsResearch & Intelligence5 min read

Key Takeaway

When a health check failed at 3am, three AI agents diagnosed the issue, identified the root cause, deployed a rollback, and generated a post-mortem — all in 8 minutes, before any human woke up.

The Problem

Production incidents at 3am are the worst kind. Not because they're technically harder — because nobody's awake.

Our stack runs on Render. Three services: eskimoai-api (core API), eskimoai-worker (background jobs), and eskimoai-dashboard (frontend). When one goes down, the others usually follow.

Before agents, a 3am incident meant:

PagerDuty wakes someone up (~5 min to become coherent)
They SSH in, check logs, understand the situation (~15 min)
They diagnose (~10-30 min, depending on complexity)
They fix or rollback (~5-10 min)
They verify recovery (~5 min)
Total: 40-65 minutes of degraded service. Plus one zombie human the next day.

I needed a system that handles the 80% of incidents that follow a pattern: deploy caused a regression, rollback fixes it. The 20% that need a human? Escalate with full context. But don't wake me up to read logs.

The Solution

Alfrawd (master agent) monitors health endpoints every 60 seconds. When a check fails twice consecutively, it activates a crisis war room with three specialized agents working in parallel.

Built on Mr.Chief's War Room skill combined with Render API integration for health monitoring and deployment control.

The Process

yamlShow code

# crisis-war-room.yaml
name: crisis-response
trigger:
  type: health_check_failure
  conditions:
    consecutive_failures: 2
    services: ["eskimoai-api", "eskimoai-worker", "eskimoai-dashboard"]

war_room:
  agents:
    thom:
      role: "Diagnostician"
      actions:
        - fetch_service_logs(last_30_min)
        - check_error_rates
        - analyze_log_patterns
        - identify_anomalies
    thom-devops:
      role: "Infrastructure"
      actions:
        - check_render_status
        - check_resource_utilization
        - check_recent_deployments
        - verify_external_dependencies
    nico:
      role: "Code Reviewer"
      actions:
        - get_recent_commits(last_6_hours)
        - diff_current_vs_last_stable
        - identify_risky_changes
        - check_migration_status

  escalation:
    level_1: "Auto-fix if confidence > 85%"
    level_2: "Alert Bilal with context if fix uncertain"
    level_3: "Full team alert if data loss risk"

  auto_remediation:
    allowed_actions:
      - rollback_to_previous_deploy
      - restart_service
      - scale_horizontally
    requires_human:
      - database_migrations
      - config_changes
      - third_party_api_issues

Here's what happened on February 14, 2026 at 3:07am:

View details

03:07:12 — Health check FAILED: eskimoai-api (HTTP 502)
03:07:42 — Health check FAILED: eskimoai-api (HTTP 502) — 2nd consecutive
03:07:43 — Crisis war room activated

03:07:44 — [thom] Fetching logs for eskimoai-api (last 30 min)
03:07:44 — [thom-devops] Checking Render service status
03:07:44 — [nico] Pulling recent deployments and commits

03:08:01 — [thom-devops] FINDING: New deployment at 01:14am (deploy_id: dep_xyz)
           Previous stable deploy: 2 days ago
03:08:15 — [thom] FINDING: OOM kills starting at 02:52am. Memory usage
           ramped from 280MB to 512MB limit over 98 minutes.
           Pattern: linear memory growth = memory leak
03:08:22 — [nico] FINDING: Deploy dep_xyz included commit abc123:
           "Add /api/v2/analytics endpoint" — new endpoint with
           in-memory aggregation buffer. No cleanup/flush logic.
           This is the leak.

03:09:00 — [vivi/synthesis] Root cause identified:
           - WHAT: Memory leak in /api/v2/analytics endpoint
           - WHEN: Deployed at 01:14am, OOM at 02:52am (98 min)
           - WHY: In-memory buffer grows unbounded (no TTL, no flush)
           - CONFIDENCE: 94%
           - RECOMMENDED ACTION: Rollback to previous deploy

03:09:15 — Auto-remediation: Rolling back to previous stable deploy
03:11:30 — Rollback complete. Service restarting.
03:12:45 — Health check PASSED: eskimoai-api (HTTP 200)
03:13:00 — Health check PASSED: eskimoai-worker (HTTP 200)
03:13:15 — Health check PASSED: eskimoai-dashboard (HTTP 200)

03:15:00 — Post-mortem generated and saved
03:15:01 — Bilal NOT alerted (resolved, non-critical, no data loss)

Total time from failure detection to recovery: 8 minutes, 3 seconds.

The post-mortem was waiting in my inbox when I woke up:

markdownShow code

## Incident Post-Mortem: 2026-02-14 03:07 UTC

**Severity:** P2 (Service disruption, no data loss)
**Duration:** 8 minutes
**Resolution:** Automated rollback

**Root Cause:** Memory leak in new /api/v2/analytics endpoint.
In-memory aggregation buffer grows linearly with requests,
no TTL or size limit. OOM kill after 98 minutes of traffic.

**Action Items:**
1. [ ] Fix memory leak: add TTL + max buffer size to analytics endpoint
2. [ ] Add memory usage alerting at 80% threshold (pre-OOM warning)
3. [ ] Require load test for endpoints with in-memory state
4. [ ] Add canary deploy step for memory-intensive changes

The Results

Metric	Before (Human On-Call)	After (Agent War Room)
Detection to diagnosis	20-35 min	1 min 17 sec
Detection to resolution	40-65 min	8 min 3 sec
Human involvement required	Always (someone wakes up)	Only for complex/data-risk incidents
Post-mortem quality	Written next day (memory fades)	Generated immediately with full logs
3am incidents requiring human	100%	~20% (complex edge cases only)
Sleep interruptions per month	3-4	0-1
MTTR (mean time to recovery)	52 min	8 min

Try It Yourself

Combine the War Room skill with health check monitoring and your deployment platform's API. Render, Railway, Fly.io — any platform with a deployment API can support automated rollback.

The key design principle: agents work in parallel, not serial. Thom reads logs while Thom-devops checks infrastructure while Nico reviews code. That parallelism is what gets you from "detected" to "diagnosed" in 73 seconds.

Start with rollback as your only auto-remediation action. It's safe, it's reversible, and it covers 80% of deploy-caused incidents. Add more actions only after you trust the system.

Start free on Mr.Chief →

The best incident response is the one that finishes before you wake up.

incident responsewar roomDevOpsmulti-agentautomation

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CTO

Crisis War Room: Render Down, 3 Services Affected — Agents Coordinated Recovery

MTTR 52 min → 8 min, zero human wake-upsResearch & Intelligence5 min read

Key Takeaway

When a health check failed at 3am, three AI agents diagnosed the issue, identified the root cause, deployed a rollback, and generated a post-mortem — all in 8 minutes, before any human woke up.

The Problem

Production incidents at 3am are the worst kind. Not because they're technically harder — because nobody's awake.

Our stack runs on Render. Three services: eskimoai-api (core API), eskimoai-worker (background jobs), and eskimoai-dashboard (frontend). When one goes down, the others usually follow.

Before agents, a 3am incident meant:

PagerDuty wakes someone up (~5 min to become coherent)
They SSH in, check logs, understand the situation (~15 min)
They diagnose (~10-30 min, depending on complexity)
They fix or rollback (~5-10 min)
They verify recovery (~5 min)
Total: 40-65 minutes of degraded service. Plus one zombie human the next day.

The Solution

Alfrawd (master agent) monitors health endpoints every 60 seconds. When a check fails twice consecutively, it activates a crisis war room with three specialized agents working in parallel.

Built on Mr.Chief's War Room skill combined with Render API integration for health monitoring and deployment control.

The Process

yamlShow code

# crisis-war-room.yaml
name: crisis-response
trigger:
  type: health_check_failure
  conditions:
    consecutive_failures: 2
    services: ["eskimoai-api", "eskimoai-worker", "eskimoai-dashboard"]

war_room:
  agents:
    thom:
      role: "Diagnostician"
      actions:
        - fetch_service_logs(last_30_min)
        - check_error_rates
        - analyze_log_patterns
        - identify_anomalies
    thom-devops:
      role: "Infrastructure"
      actions:
        - check_render_status
        - check_resource_utilization
        - check_recent_deployments
        - verify_external_dependencies
    nico:
      role: "Code Reviewer"
      actions:
        - get_recent_commits(last_6_hours)
        - diff_current_vs_last_stable
        - identify_risky_changes
        - check_migration_status

  escalation:
    level_1: "Auto-fix if confidence > 85%"
    level_2: "Alert Bilal with context if fix uncertain"
    level_3: "Full team alert if data loss risk"

  auto_remediation:
    allowed_actions:
      - rollback_to_previous_deploy
      - restart_service
      - scale_horizontally
    requires_human:
      - database_migrations
      - config_changes
      - third_party_api_issues

Here's what happened on February 14, 2026 at 3:07am:

View details

03:07:12 — Health check FAILED: eskimoai-api (HTTP 502)
03:07:42 — Health check FAILED: eskimoai-api (HTTP 502) — 2nd consecutive
03:07:43 — Crisis war room activated

03:07:44 — [thom] Fetching logs for eskimoai-api (last 30 min)
03:07:44 — [thom-devops] Checking Render service status
03:07:44 — [nico] Pulling recent deployments and commits

03:08:01 — [thom-devops] FINDING: New deployment at 01:14am (deploy_id: dep_xyz)
           Previous stable deploy: 2 days ago
03:08:15 — [thom] FINDING: OOM kills starting at 02:52am. Memory usage
           ramped from 280MB to 512MB limit over 98 minutes.
           Pattern: linear memory growth = memory leak
03:08:22 — [nico] FINDING: Deploy dep_xyz included commit abc123:
           "Add /api/v2/analytics endpoint" — new endpoint with
           in-memory aggregation buffer. No cleanup/flush logic.
           This is the leak.

03:09:00 — [vivi/synthesis] Root cause identified:
           - WHAT: Memory leak in /api/v2/analytics endpoint
           - WHEN: Deployed at 01:14am, OOM at 02:52am (98 min)
           - WHY: In-memory buffer grows unbounded (no TTL, no flush)
           - CONFIDENCE: 94%
           - RECOMMENDED ACTION: Rollback to previous deploy

03:09:15 — Auto-remediation: Rolling back to previous stable deploy
03:11:30 — Rollback complete. Service restarting.
03:12:45 — Health check PASSED: eskimoai-api (HTTP 200)
03:13:00 — Health check PASSED: eskimoai-worker (HTTP 200)
03:13:15 — Health check PASSED: eskimoai-dashboard (HTTP 200)

03:15:00 — Post-mortem generated and saved
03:15:01 — Bilal NOT alerted (resolved, non-critical, no data loss)

Total time from failure detection to recovery: 8 minutes, 3 seconds.

The post-mortem was waiting in my inbox when I woke up:

markdownShow code

## Incident Post-Mortem: 2026-02-14 03:07 UTC

**Severity:** P2 (Service disruption, no data loss)
**Duration:** 8 minutes
**Resolution:** Automated rollback

**Root Cause:** Memory leak in new /api/v2/analytics endpoint.
In-memory aggregation buffer grows linearly with requests,
no TTL or size limit. OOM kill after 98 minutes of traffic.

**Action Items:**
1. [ ] Fix memory leak: add TTL + max buffer size to analytics endpoint
2. [ ] Add memory usage alerting at 80% threshold (pre-OOM warning)
3. [ ] Require load test for endpoints with in-memory state
4. [ ] Add canary deploy step for memory-intensive changes

The Results

Metric	Before (Human On-Call)	After (Agent War Room)
Detection to diagnosis	20-35 min	1 min 17 sec
Detection to resolution	40-65 min	8 min 3 sec
Human involvement required	Always (someone wakes up)	Only for complex/data-risk incidents
Post-mortem quality	Written next day (memory fades)	Generated immediately with full logs
3am incidents requiring human	100%	~20% (complex edge cases only)
Sleep interruptions per month	3-4	0-1
MTTR (mean time to recovery)	52 min	8 min

Try It Yourself

Combine the War Room skill with health check monitoring and your deployment platform's API. Render, Railway, Fly.io — any platform with a deployment API can support automated rollback.

Start with rollback as your only auto-remediation action. It's safe, it's reversible, and it covers 80% of deploy-caused incidents. Add more actions only after you trust the system.

Start free on Mr.Chief →

The best incident response is the one that finishes before you wake up.

incident responsewar roomDevOpsmulti-agentautomation

Related case studies

CTO

Post-Mortem: Why AlphaNet Failed — Agents Analyzed What Went Wrong

When our blockchain project failed after Sprint 1, AI agents ran a structured post-mortem across technical, process, and financial domains. 5 process improvements adopted.

5 process improvements, 0 repeated failures in 6 months5 min read

Founder

Strategy Debate: Fund vs Studio — 4 Agents Argued Both Sides

4 AI agents argued both sides of a critical business decision: launch a fund or stay an AI studio. Produced a structured decision memo in 1 hour that would take consultants 2 weeks.

1-hour decision memo vs 2-week consulting engagement6 min read

Founder

War Room: 4 Agents Brainstorm the Next Product — Top 3 Ideas in 45 Minutes

4 AI agents brainstormed the next product in 45 minutes. 10 ideas generated, 5 researched, 3 ranked with go/no-go verdicts. Replaces half-day founder brainstorms.

10 ideas → 3 ranked verdicts in 45 min5 min read

Want results like these?

Start free with your own AI team. No credit card required.

Start Free →Browse agents