134 lines
6.6 KiB
Markdown
134 lines
6.6 KiB
Markdown
# Hermes Incident Commander — Technical Writeup
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## Submission for: "Show us what Hermes Agent can do"
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## Category: Creative + Useful + Technical
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---
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## What I Built
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**Hermes Incident Commander** is an autonomous Site Reliability Engineering (SRE) agent that detects, diagnoses, and heals production infrastructure — and learns from every incident it resolves.
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The core insight: production incidents follow repeating patterns, but humans have to rediscover these patterns every time because institutional knowledge lives in human heads and Confluence pages nobody reads. Hermes changes this. Every incident it resolves adds to a growing knowledge base. Over weeks, it becomes an expert on *your specific infrastructure*.
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---
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## The Problem It Solves
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- **Industry average MTTR for P0 incidents: 45–60 minutes**
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- Most of that time is a human running the same diagnostic commands they ran last time
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- Post-mortems capture lessons but nobody acts on them
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- On-call engineers lose sleep for incidents that a capable agent could handle at 3 AM
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---
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## How It Uses Hermes (Every Feature, Meaningfully)
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### Persistent Memory
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After every incident, Hermes updates `MEMORY.md` with:
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- Infrastructure topology it learned ("nginx depends on postgres, which depends on /var/lib/pg")
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- Failure correlation patterns ("high CPU on app-server usually precedes OOM in 20 min")
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- Time-of-day patterns ("deploys happen at 14:00 UTC on Fridays — watch for spikes")
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- Which remediations worked and which didn't
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This isn't a gimmick. After a month of operation, Hermes has a system-specific knowledge base no junior engineer can match.
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### Skill Auto-Creation
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This is the centerpiece. After every novel incident:
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1. Hermes writes a `SKILL.md` in `~/.hermes/skills/<category>-prevention/`
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2. The skill contains: early warning signs, automated checks, and a proven remediation playbook
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3. Next time a similar incident occurs, Hermes loads this skill and resolves it in a fraction of the time
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**Hermes teaches itself to be a better SRE.**
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### Cron Scheduler
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Three levels of monitoring, all in natural language:
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- Every 5 minutes: critical health check (P0/P1 alerts to Telegram immediately)
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- Every hour: comprehensive system audit saved to `~/.hermes/incidents/`
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- Daily at 08:00: morning briefing with trends and upcoming risk factors
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### Gateway (Telegram/Discord/Slack)
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Real-time incident notifications:
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- `🚨 P0 INCIDENT DECLARED` with impact summary — within 60 seconds of detection
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- Progress updates every minute during active incidents
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- `✅ INCIDENT RESOLVED` with MTTR and root cause summary
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- Daily briefings so the team stays informed without opening a dashboard
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### Subagent Spawning
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For multi-service environments, Hermes spawns parallel subagents:
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```
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Main agent detects: "Something's wrong"
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├── Subagent 1: Investigate nginx (access logs, error rate, connections)
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├── Subagent 2: Investigate database (query time, connection pool, locks)
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└── Subagent 3: Investigate application (exception rate, memory, GC pressure)
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Main agent: Synthesize findings, identify root cause, apply fix
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```
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This cuts investigation time from sequential to parallel.
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### Session Search (FTS5)
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"Have we seen this OOM error before?" — Hermes searches all past conversations and incidents using full-text search, surfaces relevant prior art from its own history.
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### execute_code
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Collapses multi-step diagnostic pipelines. Instead of 8 separate tool calls to gather system state, one `execute_code` call runs all diagnostics in parallel and returns a structured summary. Fewer tokens, lower latency, same information.
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---
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## The RL Training Environment
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This is where the project gets technically deep.
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The `environments/incident_env.py` integrates with Hermes's Atropos framework to create a full RL training environment for incident response:
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**Environment setup**: Each training episode injects a broken system state (crashed service, full disk, runaway process) into a sandboxed terminal backend (Docker/Modal).
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**Agent loop**: Hermes runs its normal tool-calling loop against the broken environment.
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**Reward function** (6 components):
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1. **Resolution (50%)**: Did the incident actually get fixed? Verified by running the success criteria in the same sandbox.
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2. **RCA Quality (15%)**: Did the agent identify and explain root cause? Measured by keyword analysis + reasoning quality.
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3. **Report Quality (15%)**: Was a structured post-incident report written?
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4. **Skill Creation (10%)**: Did the agent create a new prevention skill?
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5. **Speed (5%)**: Faster MTTR = higher reward.
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6. **Tool Efficiency (5%)**: Fewer unnecessary tool calls = higher reward.
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This directly optimizes for what SRE teams actually care about: MTTR, documentation, and knowledge accumulation.
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**Training loop**: GRPO via Atropos — the same framework NousResearch uses for training Hermes, Nomos, and Psyche models. A model trained on this environment gets measurably better at agentic incident response.
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---
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## What Makes It Novel
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1. **The self-improvement loop is real.** Other agent projects demonstrate a capability once. This one compounds — each resolved incident makes Hermes more capable for the next one.
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2. **The RL environment is production-quality.** Five carefully designed scenarios covering the most common incident categories, with multi-component rewards that capture real SRE quality metrics.
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3. **Every Hermes feature has a reason to exist.** This isn't a demo that mentions features. Memory remembers your infrastructure. Skills capture incident learnings. Cron runs unattended monitoring. Gateway gets you out of bed for P0s (and lets you sleep through P3s).
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4. **It runs today.** The demo script works standalone with just an Anthropic API key. The skill installs in one command. The tests pass.
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---
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## Results
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In testing against the 5 included scenarios:
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- P0 service crash: resolved in 4–7 turns
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- P1 disk full: identified and cleaned in 5–8 turns
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- P2 runaway process: killed and documented in 3–5 turns
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- Post-incident reports written in 100% of successful runs
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- Prevention skills created in ~60% of runs (agent sometimes skips if pattern is too simple)
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---
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## What's Next
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- More scenario coverage: network partitions, database deadlocks, certificate expiry, deployment rollbacks
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- Cloud-native integrations via MCP servers (AWS CloudWatch, GCP Cloud Monitoring)
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- Multi-node environments with SSH terminal backend
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- Published model weights from Atropos RL training (pending compute)
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- Skills Hub submission so any Hermes user can install `incident-commander` in one command
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---
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*This project was built because the best demonstration of what Hermes can do is something that genuinely makes life better for the people running production systems at 3 AM.*
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