The Governed Harness
A policy-governed harness that wraps your agent's loop with deterministic enforcement and auditable evidence. It doesn't replace RAG or guardrails — it completes the stack.
Every agentic framework — LangChain, CrewAI, AutoGen, Claude Code — runs the same five-step loop. Knowlytix wraps that loop with deterministic governance.
The Agent Loop
Perceive
Observe inputs & context
Reason
Interpret & extract meaning
Plan
Decompose goals into steps
Execute
Tool calls & API invocation
Evaluate
Assess outcome, decide next
Wrapped by
Governance isn't another layer in your AI stack — it's the harness around the entire agent loop.
Standard Harness vs. Governed Harness
Every agentic framework builds a harness around the LLM. The question is whether that harness can be trusted in regulated contexts. Most can't.
| Concern | Standard Harness | Knowlytix Governed Harness |
|---|---|---|
| Policy enforcement | Prompt-as-control — soft instructions, ignored under load | Policy Engine — rules encoded as math, deterministically enforced |
| Context & memory | Flat files — no schema, retrieval errors, no versioning | Geometric Memory Systems — exact lookup with SHA-256 integrity |
| Output verification | LLM-as-Judge — semantic, non-reproducible, no ground truth | Logic verifier — provably correct, 100% reproducible |
| Audit | After-the-fact logs — no formal record of which rules were checked | Audit-native — every decision is a queryable, tamper-evident triple |
The standard agentic harness relies on soft, probabilistic enforcement — the LLM is asked nicely to follow rules, retrieve the right context, and grade its own output. Knowlytix replaces each of those soft controls with a deterministic equivalent.
The Underlying Tech
Patent PendingPowered by Geometric Memory Systems (GMS)
The harness is built on a knowledge graph embedded on a high-dimensional sphere. Entities become points; relationships become rotations. Every governance decision becomes a precise mathematical calculation — exact, reproducible, with no LLM judgment in the enforcement path.
Deterministic
Same input, same decision, every time.
Zero hallucination
SHA-256 integrity on every fact retrieved.
Tamper-evident audit
Every decision is a queryable triple.
Six Layers of Defense
Each layer is independent — a failure in one cannot bypass the checks in another. Together they wrap every step of the agent loop.
Policy Engine
Rules encoded as triples in a knowledge graph, not strings in a prompt. A four-stage pipeline (rule match → graph lookup → plausibility → contradiction) decides every action.
Deterministic — same input, same decision, every time.
Behavioral Contracts
Allowed workflows are formal state machines. The agent literally cannot skip a required phase, regardless of what the LLM "decides" to do.
Like a compliance checklist that enforces itself.
Orchestration
A five-score math evaluation drives every continue / escalate / abort decision. The orchestrator routes on numeric thresholds — no LLM judgment in the control loop.
Same scores in, same routing out. Reproducible by construction.
Reasoning Consistency
Rotation-matrix composition checks multi-step reasoning chains for internal coherence. Catches the failure mode where each step looks fine in isolation but the conclusion is wrong.
Logic verification the LLM cannot do reliably on its own (0–45% accuracy).
Tool Access Gateway
Three gates before any tool executes: well-formed? permitted by policy? semantically plausible? Any failure blocks or escalates immediately.
Infrastructure says "can it happen?" Governance says "should it happen?"
Telemetry & Audit Trail
Every decision is logged as a queryable triple — claims extracted, rules matched, scores computed — with SHA-256 content addressing for tamper-evident replay.
Monitoring tells you the average. Auditing proves a specific decision.
Technical Differentiators
Mathematical, not probabilistic
Defined distance metrics with thresholds, not probability estimates from another LLM.
Per-claim, not per-response
A response with four correct claims and one critical violation is not "80% compliant."
Reproducible
Same input, same output. Deterministic enforcement, not stochastic evaluation.
Audit-native
Decision records are first-class outputs, not logging afterthoughts.
Domain-agnostic harness
The harness governs. Domain knowledge lives in configuration files.
API-first
Integrates into existing AI stacks as a governed harness around your agent loop.
Ready to deploy agentic AI you can govern?
Talk to us about a governed harness for your regulated AI deployment.