> ## Documentation Index
> Fetch the complete documentation index at: https://docs.rotavision.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Introduction

> Sakshi is the governance layer for AI agents in Indian enterprises: register every agent, witness every decision on a tamper-evident chain, bound autonomy, and produce regulator-mapped evidence.

Sakshi (साक्षी, "witness") is a governance layer that sits beside the AI agents
you already run. It does not replace your models or your orchestration. It makes
them accountable: every agent is inventoried, every decision is recorded on a
chain anyone can recompute, autonomy is bounded by policy, and the result is
evidence you can hand to an auditor or a regulator.

It is built for regulated Indian enterprises first, with clause maps for RBI
Model Risk Management, DPDP, SEBI, and IRDAI, and it runs single-tenant inside
your own environment.

## The mental model

As models become agents, the unit of governance shifts from the model to the
decision. Sakshi is organized around that idea:

<CardGroup cols={2}>
  <Card title="Register" icon="clipboard-list" href="/concepts/know-your-agent">
    Know Your Agent. A live inventory of every agent and model with an owner,
    an autonomy tier, provenance, and a verifiable identity. Nothing runs
    unregistered.
  </Card>

  <Card title="Witness" icon="eye" href="/concepts/decision-chain">
    The flight recorder. Every decision is hashed onto a tamper-evident chain
    with the model identity, tokenized inputs, and outcome, recomputable by
    anyone.
  </Card>

  <Card title="Bound" icon="shield-halved" href="/concepts/autonomy-envelopes">
    Autonomy envelopes. Each action routes to auto, human review, or block by
    stakes and confidence, with oversight telemetry and a drilled kill switch.
  </Card>

  <Card title="Vidhi & Vishwas" icon="gavel" href="/concepts/evidence-packs">
    Evidence and fairness. Regulator instruments encoded as data and scored
    against live evidence, plus declared-first fairness screens gated on
    statistical significance.
  </Card>
</CardGroup>

## How you connect

There are three ways in, and every one lands on the same chain. Sakshi never
sees a raw identifier.

<CardGroup cols={3}>
  <Card title="SDK" icon="code" href="/sdk/python">
    Wrap your calls in a few lines with the Python SDK.
  </Card>

  <Card title="Middlewares" icon="plug" href="/integrations/overview">
    Add one line to an OpenAI, Anthropic, Gemini, Bedrock, LangChain,
    LangGraph, Google ADK, CrewAI, or AWS Strands agent.
  </Card>

  <Card title="MCP" icon="server" href="/sdk/mcp">
    Govern any agent that speaks the Model Context Protocol.
  </Card>
</CardGroup>

## What to read next

<CardGroup cols={2}>
  <Card title="Quickstart" icon="rocket" href="/quickstart">
    Install the SDK, register an agent, witness a decision, and recompute the
    chain in a few minutes.
  </Card>

  <Card title="Core concepts" icon="book" href="/concepts/agent-governance">
    The ideas underneath Sakshi: the chain, autonomy envelopes, evidence packs,
    and PII tokenization.
  </Card>

  <Card title="API reference" icon="terminal" href="/api-reference/introduction">
    Every endpoint, generated from the live OpenAPI spec.
  </Card>

  <Card title="Try a sandbox" icon="flask" href="https://rotavision.com/sandbox">
    A private, synthetic-data instance to poke at, no install required.
  </Card>
</CardGroup>

<Note>
  Sakshi is single-tenant and runs in your environment, so the base URL in these
  docs is your own deployment. Examples use the public demo
  (`https://demo.rotavision.com`) where a running instance helps.
</Note>