Work begins where people already are
A request arrives in the tools your teams already live in: Teams, Slack, the web, an IDE, voice or email. Agent Platform meets it there, with no new app to adopt.
MS TeamsSlackWebIDEVoiceEmail
Products / Agent Platform
Agents that ship to production
Agent Platform is the orchestration layer Zentavor uses to build, train and run AI agents inside enterprises. Multi-agent by default, auditable by construction, on-premise when the regulator says so
Runs where your data lives: Azure, AWS, GCP or bare-metal.
0%
shorter time to production
0%
lower operational workload
0%
more predictable delivery
How Agent Platform works
One request. Six stages. One platform
Follow a single request as it travels through the stack: from the channel it arrives in, to the systems it changes, under governance the whole way. Scroll to watch each stage light up
The core plans the work
Agent Platform core decomposes a business goal into reproducible steps: tools, retries and branching, all observable. A LangGraph runtime carries approval and escalation flows from the first step.
LangGraph runtimeApproval & escalationA2A protocol
Agents do the thinking
Boring tasks run autonomously; hard tasks run with a co-pilot. Each step picks the right model for the job: Claude 4, a GPT-class model, a self-hosted model or a specialist one.
Claude 4GPT-classSelf-hostedSpecialist models
Grounded in your own data
Agents analyze documents and data (OCR, RAG over corporate stores, SQL over the warehouse) inside a single context window. Vector and hybrid search, schema linking, MCP tools.
Vector DBHybrid SQL+semanticSchema linkingMCP tools
It acts in your systems
Agent Platform doesn't just answer. It writes back to Salesforce, SAP and ServiceNow, triggers ETL and refreshes BI, with scopes enforced on every call.
CRM/ERP write-backsETL triggersBICustom tools
Auditable by construction
Every step carries approvals, scopes and an audit trail. PII detection, a reviewer UI and cost & SLA telemetry: governance your security team will recognize.
Audit logPII detectionReviewer UICost & SLA telemetry
Agent Platform coreOrchestrator
Policy guardrailsGovernance & approvals
Support agentCustomer support
Sales agentLead & deal management
Ops agentOperations & monitoring
Tools & APIsIntegrations
Model-agnostic
Best model for the job. Runs on your stack
Agent Platform isn't locked to one vendor. Each step picks the right brain, and the whole thing runs where your data already lives: your cloud, your VPC or bare-metal
Claude
reasoning
GPT-class
general LLM
Mistral
open weights
Llama
self-hosted
Specialist
fine-tuned
LangGraph
runtime
Azure
cloud
AWS
cloud
GCP
cloud
SAP
ERP write-back
Salesforce
CRM write-back
ServiceNow
ITSM
Snowflake
warehouse
Demo · pick a scenario
Watch Agent Platform decompose a real task
Click a scenario and see the agent break the job into tools, retrievals and write-backs. This is the recipe, not a live model call
agent.run #a3f91c
Banking · support ticket
resolved · 4.7s
Customer · #4471
Classifyclassifier · 6 classes0.4s
accountcardbillingfraudloangeneral
Retrieverag.kb · scope=billing1.1s
KB-118 · Card replacement policy0.41
KB-204 · Double-charge refund procedure0.91
KB-077 · Statement dispute handling0.58
Draftllm.claude-4 · grounded2.3s
Agent Platform → customer
Customer
agent.run #b7e2
AiDoc · supplier invoice
booked · 4.2s
Invoice · OCRinvoice_4471.pdf
Extracted
vendorGlobex LtdPOPO-4471net€8,400.00VAT 20%€1,680.00dueNet 30
OCRaidoc.ocr · layout-aware1.8s
Matcherp.search · open POs0.9s
PO-4310 · Globex Ltd · €6,2000.39
PO-4471 · Globex Ltd · €10,0800.97
PO-4502 · Initech · €3,5400.12
Verifyrules.tax0.5s
VAT 20% validwithin contractamount ≤ PO
Approveworkflow.route0.3s
Output · booked to SAP
journal#JV-88123payable€10,080.00due09 Jul · Net 30GL5100
agent.run #c4a1
TextToSQL · business question
answered · 3.4s
Question · analyst
Understandllm · intent0.6s
metricnet_revenuebyregionperiod2026-Q1
Link schemaschema.link · 6 tables0.4s
customersordersorder_itemsregionsproductsinvoices
Generatesql.gen · grounded1.3s
Validatesql.dry-run0.8s
syntax ok1,240 rows scannedtypes ok
Output · result
| Region | Net revenue | QoQ |
|---|---|---|
| EMEA | €4.2M | +18% |
| Americas | €3.1M | +6% |
| APAC | €1.8M | +11% |
agent.run #d9f3
Data Ops · ETL incident
recovered · 4.8s
⚠ Pipeline failed: orders_etl · stage load
Detectmonitor · orchestrator0.2s
Diagnoselogs.search · orders_etl2.1s
02:14:02INFOextract · pulled 12,402 rows from source
02:14:07INFOtransform · 12,402 → 12,402 (0 dropped)
02:14:11INFOload · connection to warehouse ok
02:14:12ERRORload · IntegrityError: null value in column "customer_id" violates not-null constraint
02:14:12DEBUGfailing row → id=8231, order_no=SO-77419, customer_id=NULL
02:14:12ERRORload · batch aborted (rollback complete)
Runbookkb.runbook0.5s
RB-04 · Source connection reset0.22
RB-12 · Null-key backfill & safe retry0.95
RB-19 · Schema drift reconciliation0.31
Actpipeline.retry · safe1.4s
Extract→Transform→Load
Output · recovered
pipelineorders_etlstatusgreenrows12,403ticket#INC-2291
agent.run #e1c7
Trading · incident brief
delivered · 6.1s
Request · trader
Gatherrag.incidents · this week1.2s
Settlement delay · EUR bookMargin-call latencyLimit breach · FX deskData-feed gap · RatesSettlement delay · USTLimit breach · EquityStale quote · CreditSettlement delay · JGB
Clusterembed · by theme0.9s
Settlement ×3
Limit breaches ×2
Data / feed ×2
Summarizellm · grounded2.6s
↳ 23 incidents · 4 desks
Comparesql · vs 4-wk avg1.0s
23 incidents · −22%
vs trailing 4-week average
vs trailing 4-week average
Output · brief delivered
incidents23vs avg−22%top themeSettlementdeliveredPDF · trader
Built for delivery
Why teams pick Agent Platform instead of assembling LangGraph from scratch
Assemble it yourself
Glue LangGraph, queues and tools together by hand.
Build approval, escalation and human-in-the-loop from scratch.
Add audit logging and cost tracking after the fact.
Re-solve deployment and security for every new use case.
Months of platform work before the first agent ships.
Agent Platform
Multi-agent orchestration with shared context out of the box.
Approval, escalation and human-in-the-loop as primitives.
Audit log and cost telemetry on every step, by construction.
On-premise ready: Azure, AWS, GCP or bare-metal, one security model.
Playbooks for six domains. From pilot to production with one partner.
Governance
Auditable by construction
Every step an agent takes is scoped, logged and reversible. It is the same trail your security team expects from any production system, not a black box
Audit trail
run #4815 · support.triage
- retrievekb:readok
- draftllm:claude-4redacted
- decidepolicy:resolveapproved
- write-backservicenow:updateok
latency4.7s
cost$0.012
SLAwithin
Scoped on every call
Agents act through least-privilege tokens, never your standing credentials.
PII detected & masked
Sensitive fields are caught and redacted before they ever reach a model.
Human in the loop
High-stakes steps wait for a named approver, with the reason logged.
Runs where your data lives
AzureAWSGCPBare-metalOn-prem / VPC
See it live
See Agent Platform in your environment
A 45-minute technical session with a delivery lead, using your IT landscape and governance model as input