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CΔUSΔLΦR

Causalor · Autonomous Control Layer · Causalor Labs

Causalor · Control Active
CMG depth: 8
semantic_drift_Δt0.000
constraint_violations0
intent_preservedTRUE
correction_injections3
causal_memory_nodes142
task_horizon24 steps
structural_guaranteeactive
Enforcement active
Live

Long-horizon code agent. 24-step task.
3 drift corrections. Intent intact.

Your agent pursues the right goal.
Until it doesn't.

Causalor is the autonomous control layer for agentic AI. It maintains a live causal memory of every commitment the agent has made, detects semantic drift before it compounds, and injects the minimum repair needed to keep the agent on a structurally valid path.

Every agentic failure is a trajectory problem. The agent went somewhere it should not have gone. Causalor makes the space of “somewhere it should not go” mathematically closed.

Request Early Access See Niyati

Coming Q3 2026 · Early access open now

What It Catches

Failures that dashboards never see.

Three failure patterns that LLM observability tools, policy engines, and trace loggers cannot detect. Causalor catches all three.

Agent completes subtask 7 perfectly. Subtask 7 contradicts a commitment from subtask 2.

signalconstraint_violations

Caught at subtask 7. Repair injected. Commitment preserved.

Multi-step research agent drifts 34% from original scope by step 18. Output looks coherent.

signalsemantic_drift_Δt

Drift detected at step 11. Corrected before it became compounding.

Code-writing agent makes a decision at step 5 that makes the architectural goal unreachable by step 12.

signalpoint_of_no_return

Structural foreclosure detected at step 4. Decision flagged before lock-in.

Core Capabilities

Four enforcement mechanisms.
One guarantee: the agent stays on path.

Semantic Drift Detection

semantic_drift_Δt

0.000

Detects when an agent's trajectory has drifted from the original intent, before the output is wrong, before any metric moves.

Intent Preservation

intent_preserved

TRUE

Mathematical guarantee that the agent's long-horizon goal remains structurally reachable across every decision step.

Precision Repair Injection

correction_injections

3

When drift is detected, Causalor injects the minimum intervention required to return the agent to a structurally valid path.

Causal Memory Graph

causal_memory_nodes

142

A persistent graph of every causal dependency the agent has established. Prevents contradictory decisions across a long task.

What Existing Tools Miss

Observability logs. Causalor enforces.

Existing tools

  • ·Langfuse, Langsmith: log traces after the fact
  • ·Bedrock AgentCore: static policy enforcement
  • ·LangChain guardrails: keyword and rule matching
  • ·AutoGen: no semantic drift detection

Causalor

  • +Detects drift before output is wrong
  • +Tracks causal commitments across the full task horizon
  • +Injects minimum repair to preserve intent
  • +Computes structural guarantee: goal remains reachable

Early Access

Be part of the first pilots.

Causalor launches Q3 2026. We are running early design partnerships with agentic AI teams now. Tell us your use case.

Request Early Access

nischay@causalorlabs.com