--- url: https://deepvue.ai/industries/ai-agents/ title: "Deepvue for AI Agents | Decisioning Infrastructure for India" description: "Decisioning infrastructure for AI agents. Deterministic identity, risk, and verification data with audit-grade traceability. India-Stack-native, agent-callable." last_modified: 2026-06-19T14:32:29.870Z --- 1. [Home](/) 2. Industries 3. AI Agents Industries  ·  AI Agents # 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 LangChain, AutoGen, CrewAI, and any agent framework that needs verified data, not generated answers [Talk to a Specialist](/contact/) [Request a Demo](/demo/) ## 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. ![DollarPe](/logos/kyc/dollarpe.png) ![iMocha](/logos/kyc/imocha.png) ![Lark Finserv](/logos/kyc/lark-finserv.png) ![NAMCO Bank](/logos/kyc/namco-bank.png) ![Nest](/logos/kyc/nest.png) ![SafeTree](/logos/kyc/safetree.png) ![SwitchMyLoan](/logos/kyc/switchmyloan.png) ![Times Internet](/logos/kyc/timesinternet.png) ![Yenmo](/logos/kyc/yenmo.png) ![DollarPe](/logos/kyc/dollarpe.png) ![iMocha](/logos/kyc/imocha.png) ![Lark Finserv](/logos/kyc/lark-finserv.png) ![NAMCO Bank](/logos/kyc/namco-bank.png) ![Nest](/logos/kyc/nest.png) ![SafeTree](/logos/kyc/safetree.png) ![SwitchMyLoan](/logos/kyc/switchmyloan.png) ![Times Internet](/logos/kyc/timesinternet.png) ![Yenmo](/logos/kyc/yenmo.png) ![DollarPe](/logos/kyc/dollarpe.png) ![iMocha](/logos/kyc/imocha.png) ![Lark Finserv](/logos/kyc/lark-finserv.png) ![NAMCO Bank](/logos/kyc/namco-bank.png) ![Nest](/logos/kyc/nest.png) ![SafeTree](/logos/kyc/safetree.png) ![SwitchMyLoan](/logos/kyc/switchmyloan.png) ![Times Internet](/logos/kyc/timesinternet.png) ![Yenmo](/logos/kyc/yenmo.png) deterministic outputs, no hallucination agent-callable tool schemas p99 < 800ms decision latency replayable audit per agent action India-Stack data sources (Aadhaar, DigiLocker, GST, UPI) DPDP-aligned consent capture for agent flows built-in PEP, sanctions, fraud screening deterministic outputs, no hallucination agent-callable tool schemas p99 < 800ms decision latency replayable audit per agent action India-Stack data sources (Aadhaar, DigiLocker, GST, UPI) DPDP-aligned consent capture for agent flows built-in PEP, sanctions, fraud screening deterministic outputs, no hallucination agent-callable tool schemas p99 < 800ms decision latency replayable audit per agent action India-Stack data sources (Aadhaar, DigiLocker, GST, UPI) DPDP-aligned consent capture for agent flows built-in PEP, sanctions, fraud screening ## 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. Verified data, not generated 01 · Determinism 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. Strict JSON schemas, never free-text Source attribution on every field Idempotent: same inputs → same answer Replayable audit per action 02 · Traceability 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. Immutable trace\_id per tool call Inputs, outputs, source captured Replay endpoint for incident review Real Indian data, not synthesized 03 · India-Stack 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. 24+ India-Stack-native endpoints Compliance maps for DPDP, RBI, PMLA 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. LLM-only agent decisioning Hallucinated PAN/GST values that look real No audit trail when the agent acts on bad data Probabilistic outputs in deterministic workflows DPDP / RBI compliance impossible to evidence Agent + Deepvue tool layer Source-attributed data, never invented Replayable trace per agent decision Deterministic JSON, idempotent across retries DPDP-compliant by default, audit-exportable Building an agent that has to decide something real? 15-min walkthrough — bring your tool schema, leave with a deterministic decisioning layer wired in. [Talk to a Specialist](/contact/) [Request a Demo](/demo/) ## 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. verify\_identity PAN, Aadhaar (masked), DigiLocker pull, voter ID, driving licence — verified against source registries. [Explore](/digilocker-api/) verify\_business GST, CIN, DIN, Udyam — validated against MCA / GSTN / MSME data with source-attributed responses. [Explore](/company-verification-api/) verify\_face Face match + liveness, anti-spoof, in-house ML. Confidence score with deterministic threshold support. [Explore](/face-match-api/) verify\_bank Penny-drop ownership + IFSC validation + UPI VPA match. Returns boolean with name-match confidence. [Explore](/bank-account-verification-api/) screen\_risk PEP, sanctions, adverse-media, court records, FIR signals. Structured risk indicators, not narrative. pull\_credit Credit reports + bureau scores via Equifax partnership. Bank statement analysis for cashflow signals. [Explore](/credit-bureau-api/) ## How agents call Deepvue. Tool registration → invocation → deterministic response → audit trail. The flow your agent framework already supports. 01 Register the tools Drop our LangChain / AutoGen / CrewAI / OpenAI tool definitions into your agent's registry. Or use the raw OpenAPI spec. 02 Agent invokes a tool When the agent reasons that it needs verified data, it calls the appropriate tool: verify\_identity, verify\_business, screen\_risk, etc. 03 Deterministic response Strict JSON schema, source attribution per field, idempotent, sub-second p99 latency. Agent reasons over verified data. 04 Trace written, replayable Every tool call captures inputs, outputs, source, timestamp. When your audit team asks "why did the agent do this," you have the answer. Native tool definitions for **LangChain, AutoGen, CrewAI, LlamaIndex, OpenAI tools**, MCP-compatible servers, and raw OpenAPI 3.1. Wire decisioning into your agent today. LangChain, AutoGen, CrewAI tool definitions in our docs. Sandbox keys for everything. Drop in, start invoking. [Talk to a Specialist](/contact/) [Request a Demo](/demo/) ## A tool call, agent-side. What an agent's tool registration and invocation looks like — LangChain example, deterministic response, replayable trace. Tool registration (LangChain) CHAIN 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 ) What you get Schemas strict JSON, no free-text Tool descriptions model-readable, when-to-call hints Trace IDs per call, replayable SDKs Python, TS, plus MCP servers Tool output the agent sees // agent invokes verify\_identity({pan: "...", ...}) { "verified": true, "identity": { "name": "", "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. DATA DPDP Act, 2023 Indian data protection. Consent, purpose limitation, data-fiduciary duties — every agent action involving personal data needs this evidenced. RBI IT Act, 2000 (Sec 43A) Reasonable security practices for sensitive personal data — encryption, access controls, breach response, agent-side data handling. AML RBI Sectoral Guidance If your agent operates inside banking / lending / payments — RBI Master Directions on KYC, fair lending, video-KYC, outsourcing. FATF SEBI / IRDAI Guidance If your agent operates in securities or insurance — sectoral rules on automated decisioning, advisor licensing, customer disclosure. DATA NIST AI RMF 1.0 US framework — Govern, Map, Measure, Manage. Deepvue's audit trail provides the evidence for the Measure and Manage layers. DATA EU AI Act (high-risk) If your agent operates in EU jurisdictions — Article 14 human oversight, Article 12 logging. Replayable traces help. SCREENING ISO/IEC 42001 AI management system standard. Risk management, transparency, traceability — Deepvue's tool layer plugs into your AIMS evidence. SCREENING Sanctions / PEP / AML UN, OFAC, EU, MHA sanctions; FATF PEP lists; adverse media — agent-callable as a single screen\_risk tool. Informational, not legal advice. Sectoral or jurisdiction-specific rules layer on top of these frameworks — Deepvue's deterministic + traceable design gives you the evidentiary substrate to evidence whichever apply. ## 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. Common failure modes 1 · Agent invents a PAN that "looks valid" 2 · Agent commits a transfer to a bank account it never verified 3 · Agent skips PEP / sanctions screening because LLM decides "low risk" 4 · Agent retries decisioning, gets different answer, no idempotency 5 · Compliance asks "why did agent approve this user?" — no trace Deepvue replaces failure modes 1-3 with **deterministic tool calls** the agent must invoke — no inventing, no skipping. Mode 4 disappears via idempotent design (same input = same output). Mode 5 is solved with **replayable trace IDs** every audit team can pull. Show me the trace replay your audit team can pull. 15-min walkthrough — call a Deepvue tool from a sample agent, replay the trace, see the audit log render. [Talk to a Specialist](/contact/) [Request a Demo](/demo/) ## 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. Day-by-day rollout 1 Day 1 — sandbox keys + tool definitions for your framework 2 Days 2-3 — register tools in your agent, run end-to-end test calls 3 Days 4-5 — wire trace IDs into your audit pipeline, replay tests 4 Day 6 — production keys, gradual rollout under flag, monitor traces ## What you get out of the box for agent decisioning. Capabilities tuned for agent invocation — strict schemas, low latency, replayable traces, framework-native bindings. Integration features Framework bindings LangChain, AutoGen, CrewAI, LlamaIndex, OpenAI tools. p99 < 800ms latency Designed for in-loop agent invocation, not batch. MCP server compatible Drop into Claude Desktop, Cursor, or any MCP client. Idempotent calls Same inputs → same answer. Safe to retry under failure. Designed for agents Strict JSON schemas Every tool returns a typed, validated payload. No free-text fields. Agent can parse and reason without LLM-side cleanup. Source attribution per field Every value carries a source pointer (DigiLocker, MCA, GSTN, etc.) and a fetched-at timestamp. The agent can cite, not invent. Latency budgets p99 < 800ms for verify\_\* tools. Rate-limit-aware retries. Caching where deterministic. Designed for agent loops, not batch. Replay endpoint Pull any historical tool call by trace\_id — inputs, outputs, source, timestamp. Plug into your audit pipeline directly. DPDP-by-default consent Tool calls capture user consent metadata where required. Indian servers by default; storage arrangements are negotiated per MSA. Confidence scores per primitive Every primitive returns a confidence signal so the agent can branch on uncertainty without parsing free text. Ship a regulator-defensible agent. Tool definitions today. Sandbox traces tomorrow. Production-ready in days. [Talk to a Specialist](/contact/) [Request a Demo](/demo/) ## Agent teams building on Deepvue. Five agent-builder profiles — each shipping into a different regulated workflow, each anchored on deterministic + traceable tool calls. Banking copilot · India Customer-onboarding agent Agent invokes verify\_identity + verify\_bank + screen\_risk in one reasoning loop. Replayable trace per onboarded customer. Compliance agent · NBFC Borrower-underwriting agent Pulls credit bureau, bank statement analysis, employment verification deterministically. No LLM-fabricated income figures. Vendor-vetting agent · enterprise KYB + court-record screen Procurement agent invokes verify\_business + screen\_risk before vendor onboarding. Audit-grade trail for procurement compliance. HR agent · BGV platform Pre-hire screening agent Agent runs employment verification + court records + reference checks. Hiring manager sees structured risk indicators, not narrative. Vertical SaaS · marketplace Seller-onboarding agent Multi-tenant marketplace agent verifies seller GST, PAN, bank, and adverse-media in one tool chain. Deterministic gate before listing goes live. ## 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. Agent-side commitments Replayable trace\_id per tool call Source attribution per data field Idempotent calls, deterministic outputs DPDP-aligned consent capture Audit-side exports Per-trace replay endpoint Bulk audit-period export SOC 2 Type II controls (in audit) ISO 27001 aligned GDPR-compatible DPA available 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. Applicable regulations 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. Pricing scales with monthly tool invocationstool mix (verify\_\* vs screen\_\* vs pull\_\*)audit retention (90 / 365 / 2555 days)latency SLA tierINR 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. [Talk to a Specialist](/contact/) [Request a Demo](/demo/) ## 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? Native tool definitions for LangChain, AutoGen, CrewAI, LlamaIndex, OpenAI tools, and MCP-compatible servers. Raw OpenAPI 3.1 spec for any framework not on that list. We're framework-agnostic at the protocol layer. Can my LLM still hallucinate values if I'm using Deepvue tools? The LLM still controls when to invoke a tool — that's the whole point of agentic systems. Deepvue's job is to ensure that \*when\* the agent invokes a verification tool, the response is deterministic and source-attributed. We can't stop the agent from skipping the tool call entirely; that's a guardrail layer concern. We can ensure the agent has a deterministic alternative to making things up. How does the trace-replay endpoint work in practice? Every tool call returns a trace\_id. GET /v1/trace/ returns the full call context — inputs, outputs, source provider, fetched-at timestamp, latency. Plug this into your agent's audit log and you have per-decision evidence on demand. Is Deepvue suitable for agents operating outside India? Deepvue's tools are India-stack-native — Aadhaar, DigiLocker, GST, MCA, UPI, Indian banks. If your agent operates exclusively outside India, you'd pair Deepvue with a non-India provider for that geography. For India + global agents, Deepvue handles India; you keep your existing global stack. What about latency? My agent loop is sensitive. p99 < 800ms for the verify\_\* tools. Some screening calls (PEP / sanctions / adverse media) can be slower depending on the screening tier. For agents that can't tolerate a slow path, async invocation with webhook callback is supported. Can I run the tools locally against an MCP server? Yes — we ship an MCP server you can drop into Claude Desktop, Cursor, or any MCP-compatible client. Same tool schema, same trace IDs, same backend. The MCP server is a thin local proxy to the hosted Deepvue tools. ## 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. [Talk to a Specialist](/contact/) [Request a Demo](/demo/)