Deterministic Control Infrastructure
Deterministic control for systems that cannot afford ambiguity.
Tajalli builds deep-tech control layers that turn evidence, authority and operational constraints into decisions that can be certified, enforced and replayed.
01The gap
Intelligence is advancing faster than operational control.
AI agents, optimizers and autonomous systems can generate increasingly powerful decisions. But a proposed action is not automatically an admissible commitment, and a plausible instruction is not automatically authorized.
Tajalli inserts a deterministic control layer before consequence.
Intelligence
Models · optimizers · agents
Proposes
Tajalli
Deterministic control layer
Certifies / Authorizes
Execution
Commitments · tool calls · operations
Acts
02Architecture
Tajalli sits where intelligence becomes consequence.
Intelligent systems may propose. Tajalli determines what may actually proceed.
- Models
- Optimizers
- Agents
- Enterprise Software
Tajalli Deterministic Control Plane
Before consequence
- 01Evidence
- 02State
- 03Authority
- 04Decision
- 05Boundary
- 06Replay
- Commitments
- Tool Calls
- Operations
- Infrastructure
03Principles
Built for decisions that must survive scrutiny.
- Source-bound
- Every decision originates from explicit evidence.
- Deterministic
- Equivalent admissible state and policy produce equivalent decisions.
- Boundary-aware
- Validity ends when the evidence or governing state crosses a relevant boundary.
- Replayable
- A decision can be reconstructed from its state, policy and evidence lineage.
- Fail-closed
- Missing evidence never becomes invented authority.
04Tajalli systems
Two control problems. One deterministic architecture.
QISTAS determines what the organization may commit to. RADM determines what an agent may execute. Both rest on the same deterministic core.
Deterministic Certification & Admissibility Infrastructure
Certify before you commit.
Deterministic certification infrastructure for operational decisions that must remain valid under changing real-world conditions.
- Evidence
- State
- Certification
- Boundary
- Re-certification
- Evidence
- State
- Constraints / Authority
- Deterministic Decision
- Boundary
- Replay
Deterministic Runtime Control for AI Agents
Authorize the action, not the language.
A deterministic runtime boundary between autonomous AI agents and consequential enterprise execution.
- Proposal
- Authority
- Decision
- Controlled Execution
- Receipt
05Evidence
Engineered to be tested, not merely trusted.
Controlled validation results. Scope and methodology are described in Assurance.
View Assurance06Application domains
Where admissibility and authority decide outcomes.
- Energy & Distributed FlexibilityCertified operational commitmentsQISTAS · Validated domain
- Enterprise AI AgentsDeterministic runtime authorityRADM · Validated domain
- Cloud & Digital OperationsControlled privileged executionArchitecture applicable
- Industrial & OT SystemsBoundary-aware control architecturesArchitecture applicable
- Regulated Enterprise WorkflowsEvidence-bound decision infrastructureArchitecture applicable
07Enterprise questions
Questions enterprises ask before deploying Tajalli.
Does Tajalli replace our existing optimization, AI or operational systems?
No. Tajalli is designed to operate between decision systems and consequential execution.
QISTAS can evaluate whether an operational commitment proposed by an existing optimizer or planning system is currently admissible. RADM can evaluate whether an action proposed by an AI agent is currently authorized.
The objective is generally to add a deterministic control layer without requiring the organization to replace its existing intelligence or optimization stack.
What does “deterministic” mean in Tajalli?
It means that the operational decision is generated from explicit state, evidence and policy rather than from model confidence or free-form probabilistic interpretation.
Under the same canonical input state and the same governing policy version, Tajalli is designed to reproduce the same decision.
The claim concerns the Tajalli decision layer. It does not imply that the external world is deterministic.
What happens when required evidence is missing or stale?
Tajalli does not convert missing evidence into assumed permission.
Depending on product configuration and operating mode, the system can refuse certification, withdraw an existing decision or require another decision path.
The governing principle: missing evidence does not become invented authority.
Is RADM another LLM guardrail?
Not in the conventional sense. Most model-level guardrails focus on generated content, prompts or responses.
RADM is designed around runtime authority and effect control. The important question is not only “What did the agent say?” but “What is the agent actually permitted to cause?”
Can an agent bypass RADM and call the tool directly?
The strongest deployment architecture places consequential execution behind an exclusive mediated boundary.
If an agent retains an independent path directly to the underlying tool, no middleware layer can truthfully claim complete execution control over that bypass path.
The deployment architecture therefore matters as much as the runtime decision engine.
How would an enterprise begin evaluating Tajalli?
The recommended first step is normally a bounded technical evaluation.
For QISTAS, this may involve shadow certification against an existing operational workflow. For RADM, it may involve placing a selected set of consequential agent actions behind a controlled mediation boundary.
Before results are evaluated, the evaluation should define:
- scope
- evidence
- policy
- baseline
- success criteria
- claim boundaries
Scope
Deterministic infrastructure for consequential systems.
Tajalli is built for organizations operating critical digital and physical systems — wherever a proposed decision becomes a real commitment or an executed action.
Enterprise enquiries
Put a deterministic boundary before consequence.
Discuss an enterprise evaluation, shadow deployment or technical integration with Tajalli.