Building NexusOps: How We Used FalkorDB to Give Autonomous AI Agents an Enterprise Brain
When a critical P0 outage strikes a production distributed system at 2:00 AM, the last thing an on-call engineer needs is a generic AI chatbot hallucinating irrelevant suggestions.
Modern LLMs struggle with incident management for one primary reason: they lack topological context. They see an alert text like "Connection pool timeout on Payment Service", but they cannot see the three upstream microservices cascading into 504 errors, the recent deployment commit that changed pool timeouts, or the incident postmortem from two months ago that solved the exact same symptom.
To solve this, we built NexusOps for the FalkorDB Graph Hacks 2026 — an autonomous multi-agent incident commander powered by a low-latency multigraph architecture.
The Architectural Vision
Rather than relying on brute-force vector search or massive context windows, NexusOps equips autonomous agents with typed, deterministic Cypher graph tools:
-
Company Brain (
nexus_company_brain): A living graph connecting microservices, caches, databases, git commits, runbooks, and engineers. -
Episodic Incident Memory (
nexus_incident_memory): A persistent memory graph of historic outages, symptoms, and verified remediation playbooks.
The 4-Agent Incident Swarm
When an alert triggers, four specialized agents coordinate:
-
Sentinel Triage Agent: Traverses upstream dependencies using variable-length paths (
[:DEPENDS_ON*1..3]) to compute blast radius and classify severity. -
Root-Cause Investigator: Executes FalkorDB
shortestPath()algorithms to bridge the symptom to recent git deployments, pinpointing the smoking gun commit. - Remediation & Comms Agent: Queries FalkorDB episodic memory for matching symptoms to pull the verified playbook, looks up the on-call engineer, and prepares the escalation dispatch.
- Memory Coordinator: Autonomously writes the newly resolved incident and verified playbook back into FalkorDB so future incidents benefit immediately.
Why FalkorDB?
FalkorDB's linear algebra engine (GraphBLAS) executes graph operations as sparse matrix operations over Redis. In our benchmarks:
- Multi-hop Blast Radius Traversals executed in 0.16 ms.
- PageRank Single-Point-of-Failure Analysis executed in 0.07 ms.
- Incident Ownership Lookups executed in 0.06 ms.
In a multi-agent swarm where agents call graph tools across multiple deliberation steps, sub-millisecond latency is the difference between an incident triage taking 2 seconds vs 30 seconds.
Key Learnings
- Constrain LLMs with Typed Graph Tools: Never let an LLM write raw Cypher for mission-critical operations. Wrapping parameterized Cypher queries in schema-validated tools guarantees zero query injection, no syntax errors, and deterministic evidence paths.
- Multigraph Topology is Crucial for Agent Systems: Keeping architectural topology separate from episodic incident memory prevents clutter while enabling unified graph querying.
- Graph Algorithms Beat Heuristics: Using native PageRank and shortest-path algorithms eliminated hundreds of lines of brittle routing code.
Check out our code and run it locally:
👉 GitHub Repository: https://github.com/praiseadesina2-stack/NexusOps
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