BLACKBOX
An AI incident investigation agent that uses a graph database to build an operations knowledge graph, automatically associating information such as services, dependencies, alerts, and deployments to perform root cause analysis and accumulate knowledge.
Timeline (last 90 days)
BLACKBOX is an AI fault investigation agent that uses FalkorDB as a persistent operations knowledge graph, connecting services, dependencies, alerts, deployments, runbooks, personnel, and historical postmortems in a single graph.
After each analysis, BLACKBOX writes structured findings (root cause, affected services, solutions, owners, and postmortem knowledge) back to FalkorDB, forming a feedback loop.
BLACKBOX's design differs from RAG approaches that rely solely on vector retrieval: RAG recalls text fragments, whereas the operations knowledge graph recalls deterministic relationship paths between entities, and the latter offers greater interpretability and precision in root cause localization.
BLACKBOX distinguishes cold investigations from hot investigations: a cold investigation starts from an alert and traverses the graph to discover relevant context; a hot investigation uses previously written postmortems and findings to recall learned knowledge and reduce unnecessary tool calls.
BLACKBOX includes a real-time investigation interface for fault response; the frontend streams agent progress and updates the graph visualization, showing currently active alerts, causal paths, related deployments and PRs, blast radius, owners, reasoning events, and written-back findings.
In BLACKBOX, the language model is responsible for choosing tools, interpreting graph query results, synthesizing evidence, and generating fault explanations, but the graph always remains the source of operations facts.
BLACKBOX includes a built-in deterministic planner as a fallback: when the model is unavailable or a request exceeds the configured tool budget, the system can still execute core graph queries in a fixed order based on the graph to complete the investigation, with fully reproducible output.
BLACKBOX's next step in planning is to integrate real-time observability, ticketing, deployment, and chat systems so that the graph stays up to date as services change and faults evolve.
Productionizing BLACKBOX also requires stronger access control, tenant isolation, rate limiting, and richer query observability.
The principle strengthened by BLACKBOX's development is that agent memory is most valuable only when it is structured and connected to the world it operates in.
All Facts (10)
The principle strengthened by BLACKBOX's development is that agent memory is most valuable only when it is structured and connected to the world it operates in.
50%UnverifiedAfter each analysis, BLACKBOX writes structured findings (root cause, affected services, solutions, owners, and postmortem knowledge) back to FalkorDB, forming a feedback loop.
50%UnverifiedBLACKBOX's design differs from RAG approaches that rely solely on vector retrieval: RAG recalls text fragments, whereas the operations knowledge graph recalls deterministic relationship paths between entities, and the latter offers greater interpretability and precision in root cause localization.
50%UnverifiedBLACKBOX distinguishes cold investigations from hot investigations: a cold investigation starts from an alert and traverses the graph to discover relevant context; a hot investigation uses previously written postmortems and findings to recall learned knowledge and reduce unnecessary tool calls.
50%UnverifiedBLACKBOX includes a real-time investigation interface for fault response; the frontend streams agent progress and updates the graph visualization, showing currently active alerts, causal paths, related deployments and PRs, blast radius, owners, reasoning events, and written-back findings.
50%UnverifiedIn BLACKBOX, the language model is responsible for choosing tools, interpreting graph query results, synthesizing evidence, and generating fault explanations, but the graph always remains the source of operations facts.
50%UnverifiedBLACKBOX includes a built-in deterministic planner as a fallback: when the model is unavailable or a request exceeds the configured tool budget, the system can still execute core graph queries in a fixed order based on the graph to complete the investigation, with fully reproducible output.
50%UnverifiedBLACKBOX is an AI fault investigation agent that uses FalkorDB as a persistent operations knowledge graph, connecting services, dependencies, alerts, deployments, runbooks, personnel, and historical postmortems in a single graph.
50%UnverifiedProductionizing BLACKBOX also requires stronger access control, tenant isolation, rate limiting, and richer query observability.
50%UnverifiedBLACKBOX's next step in planning is to integrate real-time observability, ticketing, deployment, and chat systems so that the graph stays up to date as services change and faults evolve.
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