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Model governance asked one question: is this model fit for use? An agent asks a harder one, thousands of times a day: was this specific decision fit to be made, by this agent, with this autonomy, on this data? As models become agents, the unit of governance shifts from the model to the decision. Sakshi is the layer that governs at that unit. It sits beside the agents you already run, single-tenant, inside your own environment. It does not replace your models or your orchestration and it never sees a raw identifier: personal data is tokenized at ingest before anything is stored.

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The five capabilities

Each capability owns one part of the decision, and each has its own concept page.

Register

Know Your Agent. A live registry: every agent has an owner, an autonomy tier, a blast radius, and provenance. Nothing runs unregistered.

Witness

The flight recorder. Every decision is hashed onto a tamper-evident chain that anyone can recompute. No trust in the vendor required.

Bound

Autonomy envelopes. Each action routes to auto, review, or block by stakes and confidence, backed by circuit breakers and a drilled kill switch.

Vidhi

Evidence packs. Regulator instruments encoded as data and scored against your live evidence, exported as a signed bundle you could hand an examiner.
The fifth capability, Vishwas, screens for fairness declared-first and gates flags on statistical significance. See the fairness screening guide.

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How you connect

There are three ways in, and every one lands on the same chain.

SDK

Wrap your calls in a few lines of Python: register, witness, enforce.

Middlewares

Add one line to an OpenAI, Anthropic, Gemini, Bedrock, LangChain, or LangGraph agent.

MCP

Govern any agent that speaks the Model Context Protocol, with no SDK in the agent at all.
Sakshi is built for regulated Indian enterprises first, with clause maps for RBI Model Risk Management, DPDP, SEBI, and IRDAI. Try a running instance at demo.rotavision.com, or start with the quickstart.