Agentic Incident Command Center

Splunk-grounded AI incident commander with human-approved remediation.

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Inspiration

During a serious incident, the hardest part is rarely a lack of logs. It is the time lost stitching together deployment context, application errors, traces, database pressure, edge health, security noise, and proposed remediation. AI agents can help, but only if they are grounded in trusted operational data and cannot quietly execute risky actions.

Splunk is the right evidence layer for this. Splunk MCP Server can let an AI assistant investigate operational context, but the final response should still cite events, show confidence, and preserve human approval for rollback, security rule changes, customer communications, and credential-adjacent tool calls.

What It Does

Agentic Incident Command Center asks Splunk for evidence, ranks likely incident causes, shows blast radius, and queues risky remediation for human approval instead of silently acting.

The package proves that flow through:

  • A local command-center dashboard for incident summary, ranked root cause, blast radius, approval queue, SPL queries, and MCP investigation preview.
  • Splunk-ready synthetic checkout-incident data across deployment, application, APM, database, identity/security, edge network, WAF, AI correlation, remediation, communications, ticketing, and MCP runtime domains.
  • An MCP Remediation Ledger for rollback, WAF watch, stakeholder update, ticket creation, and blocked credential-boundary attempts.
  • SPL examples, local SPL-equivalent proof results, and a Splunk app candidate with dashboard panels and saved searches.
  • Official Splunk MCP Server verification in a reproducible local Splunk Enterprise Docker proof environment using synthetic data only.

How We Built It

The local prototype uses Python standard library tooling to keep the demo portable and auditable:

  • JSONL synthetic incident event generation
  • Splunk-ready CSV export
  • deterministic risk scoring and approval-state modeling
  • root-cause evidence scoring
  • blast-radius grouping by service and signal domain
  • remediation ledger extraction
  • HTML dashboard rendering
  • SPL query examples for Splunk ingestion
  • local SPL-equivalent query emulation over the generated CSV
  • Splunk app candidate files for dashboard and saved search setup

The architecture uses Splunk as the evidence layer and Splunk MCP Server as the agent access boundary: indexed incident events become the evidence source an AI incident commander can query, while the human reviewer retains final control over high-impact remediation.

Splunk Usage

The current package includes a Splunk-ready CSV, SPL examples, a recorded local Splunk Enterprise Docker proof/readback, and an official Splunk MCP Server proof in a reproducible local Splunk Enterprise Docker environment. The proof uses synthetic checkout outage data so judges can safely reproduce the full flow without private logs, credentials, customer data, or external actions.

Verified proof: the official Splunk MCP Server was installed locally as Splunk_MCP_Server, connected through mcp-remote, exposed 10 Splunk tools, and used splunk_run_query to return five synthetic agentops_events rows with event IDs, evidence references, risk scores, and approval states. This does not claim production Splunk Cloud deployment.

Boundary phrase for all public materials: Local Splunk Enterprise Docker proof with synthetic data; production Splunk Cloud deployment is not claimed.

The intended Splunk flow is:

  1. Index data/splunk_agentops_events.csv into agentops_events.
  2. Use SPL queries to retrieve the incident timeline, root-cause evidence, remediation approval ledger, MCP investigation context, and blast radius.
  3. Use the official Splunk MCP Server through an MCP client so an AI assistant can answer questions such as: what likely caused the checkout incident, which services are affected, and which remediation needs human approval first?

For reviewer reproducibility, the repository includes a local query proof pack at reports/latest_local_spl_query_results.html and official MCP readback evidence at submission/post_action_evidence/2026-06-09_optional_live_splunk_mcp_proof_readback.md.

What Makes It Different

Most AI incident demos either summarize logs or jump straight to automation. Agentic Incident Command Center focuses on the hard middle: making the next human decision faster, safer, and backed by Splunk evidence. Splunk becomes the evidence layer for agentic operations, and MCP becomes the governed investigation boundary: MCP reads the truth, the app proposes the next step, and humans approve the change.

This is an independent hackathon project and is not an official Splunk product. Splunk and related marks belong to their respective owners.

Challenges We Ran Into

The main challenge was balancing a compelling incident story with safe public evidence. The demo needed to feel operationally real without using private logs, credentials, accounts, customer data, or real external actions. The implementation solves this with synthetic but realistic cross-domain incident events and explicit claim boundaries.

Another challenge was making MCP usage judge-verifiable. The final package includes local proof evidence for the official Splunk MCP Server, while clearly avoiding any claim that this is a production Splunk Cloud deployment.

Accomplishments That We're Proud Of

  • A complete local event-to-dashboard incident command pipeline.
  • Root-cause ranking and blast-radius outputs grounded in evidence fields.
  • A reusable MCP Remediation Ledger for human-approved incident actions.
  • Splunk-ready event exports and SPL examples.
  • Official Splunk MCP Server verification in a local Splunk Enterprise Docker proof environment, using synthetic incident data only.
  • Local SPL-equivalent query results that prove the CSV supports the intended searches.
  • A recording-friendly demo tour that keeps the public video focused and safe.
  • A Splunk app candidate with dashboard XML and saved searches.
  • Submission-ready architecture, demo script, and judge-alignment materials.

What We Learned

The strongest agentic operations pattern is not unchecked autonomy. It is evidence-grounded investigation plus explicit human approval for high-impact change. Splunk is especially strong here because it can hold the operational truth, the investigation trail, and the approval evidence in one auditable place.

We also learned that a good hackathon proof needs both a product story and a reproducibility story: judges should be able to understand the incident in minutes, then inspect the data, SPL, MCP readback, and safety boundaries behind the claim.

New During Hackathon

This project was created during the Splunk Agentic Ops Hackathon submission period. The local implementation, synthetic incident event model, dashboard, query pack, official MCP proof, public repository, demo video, and submission materials were assembled for this hackathon entry.

What's Next for Agentic Incident Command Center

  • Extend the local Splunk Enterprise Docker proof to Splunk Cloud after admin access and app-install permissions are available.
  • Add richer Splunk dashboards and MCP tool guardrails for least-privilege production-style roles.
  • Turn the MCP Remediation Ledger into workflow integrations for ticketing and ChatOps while preserving human approval and audit evidence.
  • Add larger synthetic incident scenarios across application, infrastructure, and security operations.

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