When your agent decides, it shouldn't have to guess .
Deterministic identity, risk, and verification data — agent-callable, audit-traceable, India-Stack-sourced. The decisioning layer for AI agents that ship in regulated environments.
Built for the Decisioning Layer of Agentic AI
Whether you're shipping a banking copilot, a compliance agent, or a vertical SaaS workflow runner — when your agent has to decide something real about an Indian person or business, it needs verified data, not a confident-sounding hallucination.
Three things agents need that LLMs alone can't deliver.
Foundation models are excellent at language. They are not excellent at being right about a person's PAN, a company's GST status, or whether a bank account belongs to who the user claims. That gap is where agents fail in production — and where Deepvue lives.
An agent can't approve a loan, onboard a customer, or vet a vendor on data the LLM "thinks is right." Deepvue returns deterministic outputs sourced from the registry.
When an agent does something a human regrets, the answer to "why did it do that?" can't be "the model decided." Every Deepvue call ships with a trace your audit team can replay.
If your agent operates in India — fintech, marketplaces, BGV, lending — it needs Aadhaar, DigiLocker, GST, MCA, UPI as first-class data sources. Generic providers don't have them.
Agents wired to generic web search, scraped PDFs, or model-internal knowledge can't be trusted with regulated decisions. Deepvue is the deterministic, traceable layer between an LLM's reasoning and a real-world decision in India.
Why agents trip on real-world decisions.
The places agentic systems break in production — usually around the moment they have to commit to a real decision about a real person.
Six tools every regulated agent needs in India.
Each tool comes with strict JSON schemas, source attribution, replayable trace IDs, and deterministic outputs your agent can reason over without guessing.
How agents call Deepvue.
Tool registration → invocation → deterministic response → audit trail. The flow your agent framework already supports.
Native tool definitions for LangChain, AutoGen, CrewAI, LlamaIndex, OpenAI tools, MCP-compatible servers, and raw OpenAPI 3.1.
A tool call, agent-side.
What an agent's tool registration and invocation looks like — LangChain example, deterministic response, replayable trace.
from deepvue.agents import verify_identity from langchain.agents import AgentExecutor tools = [ verify_identity, verify_business, verify_face, verify_bank, screen_risk, pull_credit ] agent = AgentExecutor( llm="claude-sonnet-4-7", tools=tools, trace=True )
// agent invokes verify_identity({pan: "...", ...})
{
"verified": true,
"identity": {
"name": "<source-attributed>",
"name_match_score": 0.97,
"pan_status": "VALID_AND_LINKED"
},
"source": {
"provider": "digilocker",
"fetched_at": "2026-04-28T18:15:03Z"
},
"trace_id": "trc_v3a7c9e2",
"replay_url": "/v1/trace/trc_v3a7c9e2",
"latency_ms": 487,
"deterministic": true
} Compliance map for agentic systems in India.
The frameworks compliance leads ask about when an agent is making decisions on real users — and how Deepvue's deterministic + traceable design maps to each.
Where production agents break — and how Deepvue plugs the gap.
Five failure modes seen across agent deployments operating on Indian users. The pattern is consistent: the agent reasons fine, then commits on bad or fabricated data.
From sandbox to production in days.
Most teams wire Deepvue into their agent in under a week. Tool definitions for every major framework, sandbox keys for every endpoint, MCP-compatible servers for any client.
What you get out of the box for agent decisioning.
Capabilities tuned for agent invocation — strict schemas, low latency, replayable traces, framework-native bindings.
Agent teams building on Deepvue.
Five agent-builder profiles — each shipping into a different regulated workflow, each anchored on deterministic + traceable tool calls.
Built so your agent can be defended in front of an auditor.
When your agent does something controversial, the answer to "why?" can't be "the model decided." It has to be a trace your audit team can replay. That's what Deepvue gives you.
Deepvue is not a regulator and does not represent itself as RBI, SEBI, UIDAI, or any government authority. Customers retain full responsibility for the regulatory framework their agent operates under. Deepvue provides the verification infrastructure, deterministic outputs, and structured audit trail — the agent-design and decisioning policy is yours.
All API interactions are protected using encryption, role-based access controls, and audit logging.
Pricing built for agent loops.
Pay per tool invocation. Idempotent retries don't double-charge. Volume bands tier monthly. INR or USD invoicing.
Agent platforms running 100k-10M tool invocations per month land in per-call ranges similar to LLM provider pricing — but for verified data, not generated text. Test for free in the sandbox before committing.
Common questions from agent-builders and compliance leads.
Real questions, asked in evaluation calls. If yours isn't here, book a 15-min walkthrough — we'll answer it live.
Which agent frameworks does Deepvue support?
Can my LLM still hallucinate values if I'm using Deepvue tools?
How does the trace-replay endpoint work in practice?
Is Deepvue suitable for agents operating outside India?
What about latency? My agent loop is sensitive.
Can I run the tools locally against an MCP server?
Is Deepvue the right decisioning layer for a regulated AI agent in India?
Deepvue provides agent-callable tools that return deterministic, source-attributed identity, business, banking, and risk-screening data for Indian users — exposed through native bindings for LangChain, AutoGen, CrewAI, OpenAI tools, and MCP servers. Every tool call ships with a replayable trace_id so audit teams can answer "why did the agent do that?" with verified evidence rather than model rationalization. Designed for production-grade agentic systems operating under DPDP, IT Act, RBI, SEBI, and IRDAI frameworks.
When your agent decides,
it shouldn't have to guess.
Deterministic. Traceable. India-Stack-native. Agent-callable.