The deterministic truth layer for AI agents.
Contradictions detected by graph queries, not LLM judgment. Conflicts scored, surfaced, and resolved before any action ships. Every resolution strengthens the consensus model.
Automatically created knowledge graphs are 30–60% accurate. Entities get duplicated. Facts contradict. System prompts and guardrails SDKs don't fix these problems — they mask them.
An LLM follows constraints the way it follows a writing style — probabilistically. It has no structural mechanism to verify whether its output actually satisfies the rules it was given.
A policy established at step 1 is gone by step 5. Context compresses. System prompts get overridden. The longer the task, the less the agent remembers its rules.
Step 7 of 10 fails. Steps 1–6 already wrote to production. There's no saga, no compensation, no undo. Partial state propagates through every downstream agent.
Agent A retrieves a budget of €500K. Agent B retrieves €750K from a different source. Neither knows the other exists. Both proceed. The wrong number ships.
"Do not modify production data" is a natural language instruction, not a structural gate. It can be overridden by a longer context, a cleverer prompt, or simply lost mid-task.
EU AI Act Article 12 requires automatic event logging for high-risk systems. HIPAA requires access audit trails. LLM conversation logs do not meet either standard.
The higher the stakes, the more it matters — but the structural problem is the same everywhere. Agents that act on unverified, contradictory knowledge produce confidently wrong results.
Each gate runs formal graph queries against the consensus-scored knowledge graph — not an LLM call. When confidence drops below threshold, Brain escalates to a domain expert.
Before the agent starts, Brain queries the consensus graph to validate task scope and knowledge state. Returns ALLOW, BLOCK, or REQUIRE_APPROVAL — a deterministic verdict, not an LLM opinion.
Three lines to integrate. Behind them: 13 parallel conflict detectors, consensus scoring, domain-configurable thresholds, and a sealed audit ledger. Start in advisory mode — harden to strict when ready. Model routing included: trusted queries use cheaper models, contested queries escalate.
If Brain handles the hardest cases — conflicting clinical data, contested financials, multi-jurisdiction compliance — it handles yours.
A clinical agent retrieves conflicting drug interaction data from two trials. Brain detects the contradiction, blocks the pharmacy write, and escalates to the attending physician with both sources scored.
An underwriting agent pulls credit data from two sources that disagree on revenue. Brain scores both claims by source authority, holds the credit decision, and surfaces the conflict with full provenance.
A compliance agent monitors policy changes across 40+ jurisdictions. Brain enforces action classification — recommend → draft → submit — with domain-configurable escalation at each level.
Pre-built profiles for EU AI Act, HIPAA, SOX, GDPR, and PCI DSS.
See it with your stack →See Brain detect conflicts, enforce consensus, and seal audit trails — with your agent stack, in your infrastructure.
