IncidentAgent

When PagerDuty fires at 3am, IncidentAgent investigates before you wake up.

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Inspiration

The idea came from a painful reality in DevOps: when a production alert fires at 3am, a senior engineer wakes up and spends the next 30–60 minutes doing work that is mostly mechanical — grepping logs, querying Prometheus, checking recent deploys, cross-referencing runbooks — before they can even form a hypothesis. Enterprise downtime costs $5,600 per minute, yet the investigation itself is largely a pattern-matching exercise that an AI should be able to do.

Research backed this intuition. A Microsoft FSE 2024 study on LLM-based root cause analysis showed that ReAct-style tool-augmented agents outperform plain RAG for incident investigation. An Alibaba paper on RCAgent demonstrated that multi-agent systems can reliably identify root causes in cloud environments. An AIOps framework study published in 2025 showed a 33% reduction in MTTR when AI-assisted investigation was applied. These weren't just academic results — they described the exact problem we live with every day.

The second source of inspiration was studying what already exists. HolmesGPT (CNCF Sandbox, 1.8k stars) has a great single-agent investigation loop but no multi-agent routing, no custom-trained models, and no institutional memory. We saw a clear opportunity to build something more capable on top of DigitalOcean Gradient™ AI's full stack.

What it does

IncidentAgent is an autonomous multi-agent system that handles the entire DevOps incident response lifecycle — from raw alert to actionable remediation — in under 2 minutes.

When an alert fires (from Prometheus, PagerDuty, a webhook, or manually), IncidentAgent:

  1. Triages the alert — classifies its type (error rate, latency spike, crash, resource exhaustion, dependency failure, or config change), severity, and affected services
  2. Builds an investigation priority queue — research shows 80% of incidents correlate with recent changes, so a deployment-first queue is used for error rate alerts, K8s-first for crashes, and so on
  3. Orchestrates 6 specialist sub-agents — DeployAgent, LogsAgent, MetricsAgent, K8sAgent, RunbookAgent, and MemoryAgent — in an iterative refinement loop where each agent builds on the previous agent's findings
  4. Stops early when confidence reaches 85%, avoiding wasted investigation cycles
  5. Synthesizes evidence into ranked root cause hypotheses with confidence scores, incident timelines, and blast radius calculations
  6. Generates safe remediation steps with risk scoring, rollback plans, and human approval gates for high-risk actions

Results

Metric Manual IncidentAgent Improvement
Investigation time 50+ hours (industry avg) < 2 minutes 99.9% faster
Root cause accuracy ~60% 85%+ +25%
Time to first response 15 min 30 sec 30x faster

How we built it

DigitalOcean Gradient™ AI is the backbone of the entire system. Every major feature is deeply integrated:

Agent Development Kit (ADK)

The @entrypoint decorator marks the main investigation pipeline as the Gradient entry point. Every agent and tool is instrumented with @trace_tool, @trace_llm, and @trace_retriever decorators for full observability — so every log search, LLM synthesis call, and KB retrieval appears in the Gradient trace view.

@entrypoint
async def main(input: Dict[str, Any], context: Dict[str, Any]) -> Dict[str, Any]:
    """Main Gradient ADK entrypoint — receives alert, returns investigation result"""

@trace_tool("investigation-pipeline")
async def investigate_alert(alert: UnifiedAlert) -> InvestigationResult:
    """Full pipeline: triage → investigate → synthesize → remediate"""

Knowledge Bases

Two KBs power the system's memory:

  • kb_runbooks — static troubleshooting playbooks queried by RunbookAgent when generating remediation
  • kb_incidents — a growing dynamic store; every resolved incident gets added, giving MemoryAgent an ever-improving corpus of "what worked last time."

Both agents use a KB-first, mock-fallback pattern so the system degrades gracefully if KB connectivity is unavailable.

Agent Routing

The InvestigatorMaster dynamically routes between 6 specialist sub-agents using a priority queue seeded by the TriageAgent's alert classification. This isn't static — each sub-agent can suggest which agent should run next via the suggests_next_agent field in AgentEvidence. An LLM step re-evaluates the queue after each agent run, implementing the iterative refinement communication pattern identified as optimal in published multi-agent AIOps research.

# Alert-type → investigation priority mapping (research-backed)
INVESTIGATION_PRIORITY = {
    AlertType.ERROR_RATE:  ["DeployAgent", "LogsAgent",   "MetricsAgent", "K8sAgent"],
    AlertType.LATENCY:     ["MetricsAgent", "LogsAgent",  "DeployAgent",  "K8sAgent"],
    AlertType.CRASH:       ["K8sAgent",    "LogsAgent",   "MetricsAgent", "DeployAgent"],
    AlertType.RESOURCE:    ["MetricsAgent", "K8sAgent",   "LogsAgent",    "DeployAgent"],
}

Function Calling

Each sub-agent declares its own tool suite via get_tools():

Sub-Agent Tools
LogsAgent Elasticsearch queries with structured time windows
MetricsAgent Prometheus queries with anomaly detection
K8sAgent Pod status, events, OOM kills, restart counts
DeployAgent Kubernetes rollout history, ConfigMap diffs
RunbookAgent Gradient KB semantic search
MemoryAgent Gradient KB similarity search on past incidents

Guardrails

RemediationGuardrails applies pattern matching to every generated command:

  • Hard-blocked patterns: rm -rf, DROP DATABASE, kubectl delete namespace, terraform destroy → raises GuardrailViolation
  • Risk-elevated patterns: kubectl scale --replicas=0, ALTER TABLE, UPDATE ... SET → sets requires_approval = True
  • Any remediation plan with high-risk steps requires human approval before execution
  • High-risk steps without a rollback_plan are rejected at the schema level

GPU Training

A custom TF-IDF + Logistic Regression log anomaly classifier is trained on synthetic DevOps log data using Gradient GPU Droplets. A ModelVersionManager handles versioned promotion — a candidate model only replaces production if it scores at least 2% better on the evaluation benchmark, preventing regressions while enabling continuous improvement.

Evaluation

15+ test cases in tests/eval_dataset.csv covering all alert types. tests/eval_runner.py benchmarks accuracy, confidence scores, and investigation latency automatically via the Gradient evaluation framework.

Full Gradient Feature Map

Gradient Feature Component Code Location
Agent Development Kit Main pipeline + all agents incidentagent/main.py, all agent files
Knowledge Bases Runbook search + incident memory agents/sub_agents/runbook.py, agents/sub_agents/memory.py
Agent Routing InvestigatorMaster priority queue agents/investigator.py — _select_next_agent()
Function Calling All 6 sub-agents agents/sub_agents/*.py — get_tools()
Guardrails Remediation safety agents/remediation.py — RemediationGuardrails
GPU Training Log anomaly classifier models/train_classifier.py, models/log_classifier.py
Evaluation Benchmark suite tests/eval_dataset.csv, tests/eval_runner.py
Serverless Inference All LLM calls Anthropic Claude via Gradient

Tech Stack

digitalocean-gradient · gradient-adk · python 3.11 · fastapi · streamlit · pydantic · docker · elasticsearch · scikit-learn · structlog

Challenges we ran into

Safe multi-agent orchestration without infinite loops. When sub-agents can suggest which agent to run next, you risk cycles. We solved this by treating agents_remaining as a consuming queue — each agent can only be called once per investigation — and letting the LLM re-rank the remaining agents rather than freely nominating any.

KB-first development without live credentials. We couldn't always have Gradient KB credentials available during development. The KB-first / mock-fallback pattern (try KB → except → return rich mock data) meant development could proceed against realistic data while production seamlessly uses real KB results.

Guardrail calibration. Early versions blocked too aggressively — common operational commands like kubectl rollout restart were getting flagged. We iterated to a tiered system: hard-block for genuinely destructive patterns, risk-elevation for reversible-but-careful operations, and clean pass-through for safe commands.

Schema-driven evidence synthesis. Free-form agent outputs made downstream synthesis unreliable. The breakthrough was enforcing AgentEvidence and Finding Pydantic schemas strictly — once all agents produced structured, typed output, the synthesis LLM call became dramatically more reliable.

Accomplishments that we're proud of

  • A complete, production-ready multi-agent architecture where every layer — triage, investigation, synthesis, remediation — is independently testable and replaceable
  • The iterative refinement loop with early stopping, grounded in published academic research rather than intuition
  • Full safety separation between analysis and remediation: the investigation pipeline has zero ability to execute anything; the remediation pipeline is gated by guardrails and human approval
  • A Pydantic schema system covering 9 data models that enforces correctness across all agent boundaries: UnifiedAlert · TriageResult · AgentEvidence · Finding · InvestigationState · RootCauseHypothesis · Remediation · StoredIncident · AppSettings
  • A continuous learning loop: incidents resolved today become training data tomorrow, and the custom model only promotes if it demonstrably improves on the benchmark

What we learned

Multi-agent architectures live or die by their schemas. The single best investment we made was designing the AgentEvidence schema before writing any agent code. Every field — suggests_next_agent, early_stop_recommended, is_root_cause_candidate — was designed specifically to serve the orchestration layer above it.

Iterative refinement outperforms broadcast for incident investigation. Broadcast patterns (where a master agent sends the same context to all sub-agents simultaneously) produce redundant, uncoordinated findings. Iterative refinement — where each agent sees the previous agent's conclusions — enables findings to compound. A DeployAgent finding of "deployment 2 hours ago" becomes the LogsAgent's search window, which becomes the MetricsAgent's anomaly anchor.

Safety must be designed at the schema level, not just at the prompt level. Prompt-based guardrails ("don't suggest destructive commands") are fragile. Schema-enforced guardrails (requires_approval: bool, rollback_plan: Optional[str]) are structural — the system literally cannot generate a high-risk remediation step without a rollback plan field populated.

The KB-first pattern is essential for production AI systems. Any system that depends on an external knowledge source needs a graceful degradation path. Our mock-fallback approach meant the system always produced useful output, even in degraded conditions — which is exactly what you need in a production incident response tool.

What's next for IncidentAgent

  • Live webhook integrations — direct PagerDuty and Opsgenie alert ingestion so the system activates with zero manual input
  • Auto-remediation with staged rollout — safe remediations (pod restarts, cache flushes) execute automatically in staging before requiring approval for production
  • Continuous KB enrichment — every resolved incident is automatically summarized and added to kb_incidents, creating a compounding institutional memory with no manual effort
  • Multi-service blast radius — extend investigation across full service dependency graphs, not just the directly-affected service
  • Multi-tenant SaaS — deploy on DigitalOcean App Platform with team workspaces, per-team Knowledge Bases, and role-based approval workflows for enterprise incident response

Built with ❤️ for the DigitalOcean Gradient™ AI Hackathon — March 2026

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