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Platform · AI Agent Orchestration

AI agents can reason.
They cannot
govern themselves.

Every enterprise is deploying AI agents. The agents can analyse, recommend, and even decide. What they cannot do — on their own — is operate inside a real enterprise: with approval chains, audit trails, compliance rules, human oversight, and outcome accountability. Hubler is the governance and execution layer that makes AI agents enterprise-ready.

See it in action → Explore the platform
Works with any AI model or agent framework
Full audit trail on every agent action
Role-based permissions on every agent action
The Problem

AI agents are powerful. Enterprise operations are
not designed to trust them.

Procurement has approval limits. Finance has regulatory requirements. Compliance has mandatory sign-off chains. Operations has SOPs. An AI agent that bypasses these structures is not useful in an enterprise — it is a liability. The question is not whether AI agents are capable. It is whether the enterprise has the infrastructure to hold them accountable.

73%

Most enterprise AI pilots stall before production. The cited reason is almost never the model. It is the inability to integrate with existing governance, approval, and compliance structures.

0

No major enterprise AI framework ships with built-in approval chains, policy enforcement, or structured audit trails for agent actions out of the box.

custom integrations required to connect agent output to the actual systems — ERP, procurement, finance, HRMS — where execution happens.

The Five Gaps

Why AI agents stall before
they reach your operations.

The gap between an AI agent's capability and enterprise deployment is not a model problem. It is an infrastructure problem — five specific missing layers.

Gap What happens without it What Hubler provides
Governance Layer Agent actions bypass approval chains and policy thresholds. Compliance exposure grows with every autonomous step. Configurable approval chains, value thresholds, and policy rules applied automatically to every agent-initiated action before execution.
Execution Context Agents can recommend. They cannot send a PO, route a contract, or update a supplier record. Output stops at a document. Native connectors to ERP, procurement, finance, and operations systems so agent decisions become executable actions immediately.
Human Oversight Fully autonomous action in regulated contexts creates unacceptable risk. Partially manual override is fragile and untraceable. Configurable human-in-the-loop checkpoints — escalation to the right person, at the right time, with full context of what the agent decided and why.
Audit Infrastructure When regulators, auditors, or internal reviewers ask what the AI decided and why, there is no structured record to produce. Immutable, structured audit log on every agent decision, human approval, system action, and outcome — exportable on demand.
Outcome Feedback Agents operate in a one-way direction. Outputs go into a black box. The AI cannot learn from what actually happened in operations. Structured outcome data fed back into the agent's next decision cycle — closing the loop between AI recommendation and operational reality.
How It Works

Hubler sits between your AI
and your enterprise systems.

Your AI models and agents generate intelligence. Your enterprise systems execute and record. Hubler is the governed layer in between — translating AI decisions into enterprise actions with every policy, approval, and audit trail enforced automatically.

AI Models & Agents
LLM / Foundation Models
GPT-4, Claude, Gemini, Llama
Agentic Frameworks
LangChain, AutoGen, CrewAI
Custom AI Models
Internal models & predictions
Planning AI
Forecasts, demand signals, plans
Copilots & AI Assistants
Microsoft Copilot, Google Duet
Hubler · Governance & Execution Layer
Policy Enforcement
Approval chains, value limits, compliance rules applied before any action executes
Role-Based Access Control
Granular permissions at the agent, workflow, and action level
Human-in-the-Loop
Configurable escalation to the right approver with full agent context
Execution Routing
Agent decisions converted to structured actions routed to the right system
Audit & Traceability
Immutable record: what the agent decided, who approved it, what happened
Outcome Feedback Loop
Structured outcomes returned to AI for continuous improvement
Enterprise Systems
ERP
SAP, Oracle, Microsoft Dynamics
Procurement Systems
Coupa, Ariba, Jaggaer
Finance & GRC
Workday, Oracle Financials, GRC platforms
Operations Systems
WMS, field ops, store management
Platform Capabilities

Everything an enterprise needs
to run AI agents at scale.

Hubler provides six core capabilities that transform raw agent output into governed, auditable, enterprise-grade execution.

Governance Engine

Define approval chains, spending limits, compliance gates, and policy rules that apply automatically to every AI agent action — before anything executes.

Human-in-the-Loop

Configure exactly when humans must review AI decisions, who receives escalations, and what context they see. Approval — or override — in seconds, not email chains.

Execution Connectors

Pre-built connectors to SAP, Oracle, Coupa, Ariba, Workday and 50+ enterprise systems. Agent decisions become real actions in real systems — automatically, without custom integration work.

Immutable Audit Trail

Every agent decision, human approval, system action, and outcome is recorded in a tamper-proof, timestamped, structured audit log — exportable for compliance or regulatory review.

Outcome Feedback Loop

Structured outcomes — what actually happened after the agent acted — are fed back into the AI model's next decision cycle, improving recommendations over time.

Operational Visibility

Real-time dashboards showing every active agent workflow, pending approvals, completed actions, and outcome metrics — giving operations leaders full visibility without log-diving. No black boxes. No blind spots.

Agent Scenarios

How Hubler governs AI agents
across your enterprise.

Illustrative step-by-step walkthroughs of how Hubler operationalises AI agent decisions in four core enterprise domains.

Procurement AI Agent

An AI agent analyses purchase history, current inventory levels, and demand forecasts to generate an optimal purchase order recommendation for a high-velocity SKU.

7-Step Execution Flow
1
Agent generates PO recommendation
AI analyses stock levels, lead times, and demand signals. Outputs: vendor ID, SKU, quantity, proposed price — structured as a Hubler execution request.
2
Hubler validates against policy
Checks order value against approval thresholds. Confirms vendor is on the approved vendor list. Validates SKU against procurement policy. Flags any policy breach before proceeding.
3
Approval routed to procurement manager
Order value exceeds auto-approval limit. Hubler routes to the designated procurement manager with full context: agent rationale, stock projection, vendor history, and policy check results.
4
Manager approves in Hubler mobile
One-tap approval with optional comment. Approval decision, approver identity, and timestamp are recorded in the audit log. Manager can also adjust quantity or request vendor re-quote.
5
PO created in SAP and sent to vendor
Hubler creates the PO in SAP via native connector, generates PDF, and sends to vendor via configured channel (email, EDI, or vendor portal). All in the same workflow step.
6
Vendor acknowledgement tracked
Hubler monitors for vendor confirmation. If no response within the SLA window, automatic escalation is triggered. Confirmation status is visible in real-time on the procurement dashboard.
7
Outcome fed back to AI model
Actual delivery time, quantity received, price variance, and quality outcome are structured and returned to the AI model — improving the next procurement recommendation cycle.
Audit Record Summary

Agent decision · Policy check passed · Approval requested 09:14 · Approved by A. Singh 09:22 · PO #PO-2024-4821 created in SAP · Vendor confirmed 11:40 · Goods received Day 6 · Quantity variance: 0 · Outcome score returned to AI.

Compliance AI Agent

An AI agent continuously monitors vendor documentation, contract expiry, and regulatory filing deadlines — generating compliance action items with urgency scoring across a 200-vendor portfolio.

7-Step Execution Flow
1
Agent identifies compliance gap
AI detects that 12 vendors have certifications expiring within 30 days. Generates remediation actions: renewal requests, document uploads, and escalation notices — structured for Hubler ingestion.
2
Hubler classifies by urgency and policy
Each compliance action is cross-referenced against vendor tier, contract value, and regulatory requirement. Critical gaps — those affecting active contracts or regulated suppliers — are flagged for immediate escalation.
3
Vendor notification workflows triggered
Hubler sends structured renewal requests to each vendor with specific document requirements, upload links, and deadlines. All communications logged to the audit record with send timestamp and delivery confirmation.
4
Compliance officer assigned critical cases
For vendors with regulatory exposure, Hubler assigns a compliance officer as the responsible owner with a defined resolution SLA. Officer receives full vendor context and a structured action checklist.
5
Documents received and validated
As vendors upload documents, Hubler routes to the assigned reviewer for validation. AI agent performs initial format and completeness check. Accepted documents are versioned in the vendor compliance record.
6
Escalation on non-response
Vendors that miss the 14-day response window are automatically escalated: procurement manager notified, PO release for that vendor paused pending resolution, legal flagged if contract risk threshold is met.
7
Compliance status updated and audit closed
Upon validation, vendor compliance record is updated across the system. Full audit trail — agent detection, human review, vendor response, documents received, approval — is available for regulator export.
Audit Record Summary

Agent scan 07:00 daily · 12 gaps identified · 8 vendor notifications sent · 3 compliance officers assigned · 9 of 12 resolved within SLA · 2 vendors escalated · 1 PO hold applied · Full audit trail exportable for regulatory review.

Finance AI Agent

An AI agent analyses invoice data, contract terms, and budget positions to identify early payment discount opportunities and flag invoices at risk of breaching budget allocations.

7-Step Execution Flow
1
Agent identifies early payment opportunity
AI identifies 3 invoices where early payment within 7 days yields a 2% discount totalling ₹4.2L. Structures a payment acceleration request for Hubler with supporting rationale.
2
Hubler checks budget and cash position
Validates available budget in the relevant cost centres. Cross-checks cash flow forecast. Confirms accelerated payment does not breach treasury policy. All checks logged with result and timestamp.
3
CFO approval requested for above-threshold
Two invoices are within finance manager authority; one exceeds the threshold and routes to the CFO. CFO receives AI rationale, discount calculation, budget impact, and a one-click approval path.
4
Approvals completed within 40 minutes
Finance manager approves two invoices via Hubler. CFO approves the third within the same morning. All three approval decisions recorded with approver identity, time, and any attached comments.
5
Payment instructions issued in Oracle
Hubler triggers payment release in Oracle Financials via native connector. Payment reference numbers returned and linked back to the Hubler workflow record for complete end-to-end traceability.
6
Vendor payment confirmation tracked
Hubler monitors for vendor acknowledgement and bank confirmation. Discrepancies between expected and confirmed amounts trigger an alert to the finance team for resolution.
7
Discount capture and ROI fed back to AI
Actual discount captured, vendor satisfaction, and treasury impact are structured and returned to the AI model — improving future opportunity identification and timing recommendations.
Audit Record Summary

Agent opportunity identified 08:30 · Budget check passed · 2 approvals by Finance Manager 08:52 · CFO approval 09:10 · Payment issued Oracle ref: PAY-8821, PAY-8822, PAY-8823 · Discount captured: ₹4.2L · Outcome returned to AI model.

Operations AI Agent

An AI agent monitors retail store performance metrics in real time, identifying stores deviating significantly from plan and generating targeted operational interventions for store managers and regional leads.

7-Step Execution Flow
1
Agent detects store performance deviation
AI identifies Store #47 is 38% below daily sales plan by 14:00. Cross-references stock levels, footfall data, and staffing schedule. Generates a structured intervention request for Hubler.
2
Hubler validates against operational SOP
Checks intervention type against store SOP. Confirms store manager is on shift. Validates proposed intervention actions — stock reallocation, staff re-deployment, promotional trigger — against policy rules.
3
Store manager notified with action plan
Store manager receives Hubler alert on mobile: AI diagnosis, recommended actions, and a structured checklist. Manager can accept the full plan, accept with modifications, or escalate to regional lead.
4
Manager executes and confirms actions
Manager executes the intervention and marks each action complete in Hubler. Photo evidence, manager notes, and completion timestamps are captured for each step in the action plan.
5
Regional lead oversight maintained
Regional manager has real-time visibility of the intervention across all affected stores — status of each action, completion rate, and early performance signal — without requiring calls or status meetings.
6
Performance tracked post-intervention
Hubler monitors Store #47 performance for the following 4 hours. If recovery trajectory is below expected improvement, a follow-up escalation is triggered automatically to the regional lead.
7
Intervention effectiveness returned to AI
Actual sales recovery, time-to-action, and which interventions were effective are structured and returned to the AI model — improving the next store performance prediction and intervention recommendation.
Audit Record Summary

Agent alert 14:02 · SOP check passed · Store Manager notified 14:03 · All 4 actions completed 14:31 · Sales recovery +22% within 2 hours · Regional lead oversight maintained throughout · Effectiveness data returned to AI.

Use Cases

Where enterprises deploy
Hubler-governed AI agents.

Procurement

Autonomous PO Generation

AI generates purchase orders based on demand signals. Hubler enforces approval thresholds, vendor compliance, and budget checks before execution.

Compliance

Vendor Compliance Monitoring

AI monitors certification and regulatory deadlines. Hubler triggers remediation workflows, assigns ownership, and maintains a complete compliance audit trail.

Finance

Invoice & Payment Automation

AI identifies payment opportunities and exceptions. Hubler routes approvals, issues payment instructions to ERP, and captures outcomes back to the AI.

Operations

Store & Field Operations

AI detects operational deviations. Hubler delivers structured action plans to field teams, tracks execution, and reports recovery outcomes.

Contract Management

Contract Lifecycle Automation

AI flags renewal, renegotiation, and risk events in contracts. Hubler routes to legal and commercial stakeholders with structured review workflows.

HR & People

Workforce Planning Execution

AI generates staffing recommendations based on forecast. Hubler routes approvals, triggers onboarding workflows, and tracks deployment against plan.

Supply Chain

Supply Chain Risk Response

AI identifies supply disruption signals. Hubler triggers procurement diversification workflows and coordinates cross-functional response with documented escalation chains.

Audit & Reporting

Regulatory Audit Preparation

AI identifies gaps before an audit cycle. Hubler provides structured, exportable audit records covering every agent action, human decision, and system outcome.

Architecture

Designed for the enterprise
from the ground up.

Hubler's agent orchestration layer is built on the same enterprise-grade infrastructure that governs 4M+ workflow executions across 100+ customers worldwide.

Multi-Tenant Isolation

Each enterprise's agent data, audit logs, and policy configurations are fully isolated. No cross-tenant data access, ever.

Role-Based Access Control

Granular permissions at the agent, workflow, and action level. Approvers see only what they need. Auditors see everything they must.

Immutable Event Log

Every event — agent decision, policy check, approval, system action, outcome — is written to a tamper-evident, append-only log with cryptographic integrity.

Webhook & API Integration

Any AI model or agent framework can connect to Hubler via REST API or webhook. Structured JSON input, structured outcome output. No proprietary SDK required.

SLA-Driven Execution

Every action has a configurable SLA. Missed SLAs trigger automated escalation — no manual monitoring required to ensure agent workflows complete on time.

ISO 27001 Certified

Hubler is ISO 9001 and ISO 27001 certified. Data residency options available for enterprises with regional regulatory requirements.

Audit Trail

Every action. Timestamped.
Traceable. Exportable.

When your auditor, regulator, or board asks what the AI decided and who approved it, Hubler produces a complete, structured record instantly.

Audit Log — Procurement Workflow #PW-2024-4821
SKU: HUB-00441 · Vendor: Apex Supplies Ltd · Value: ₹8,40,000
Completed ✓
08:14:02 UTC
AI_AGENT generated PO recommendation · Qty: 500 · Price: ₹1,680/unit · Rationale: stock forecast 6-day depletion, lead time 4 days
08:14:03 UTC
POLICY_CHECK initiated · Value ₹8.4L exceeds auto-approval limit (₹5L) · Vendor status: Approved · SKU policy: Compliant
08:14:03 UTC
APPROVAL_REQUESTED → Ananya Singh (Procurement Manager) · Mobile push notification sent
08:22:17 UTC
APPROVED · Ananya Singh · "Standard reorder, approve" · IP: 192.168.x.x · Session: mobile-app-v4.2
08:22:18 UTC
SAP_ACTION PO created · Doc #4500012821 · SAP system: PRD · Confirmation received
08:22:20 UTC
VENDOR_NOTIFICATION PO #PO-2024-4821 sent to vendor@apexsupplies.com
11:40:05 UTC
VENDOR_CONFIRMED · Delivery ETA: Day +4 · Qty confirmed: 500
Day+4 14:30 UTC
OUTCOME_FEEDBACK GRN received · Qty: 500 · Variance: 0 · Quality: Pass · Outcome score returned to AI model
Why Hubler

Traditional AI platforms vs
Hubler for the enterprise.

AI platforms are built to help you build and deploy AI. Hubler is built to make AI operate inside a governed enterprise. The two are not the same problem.

Capability Traditional AI Platforms Hubler
Approval Chains Not included. Must be built on top of the platform or integrated separately. Built-in. Multi-tier, value-threshold, role-based approval chains configured without code.
Policy Enforcement Requires custom implementation. No native understanding of enterprise compliance rules or spending limits. Native policy engine. Procurement limits, compliance gates, and SOP rules enforced automatically on every action.
Enterprise System Integration API access only. Teams must build and maintain each integration themselves. Pre-built connectors to SAP, Oracle, Workday, Coupa, and 50+ enterprise systems — maintained by Hubler.
Human-in-the-Loop Pattern must be built from scratch. No native concept of escalation routing or approval context. Configurable human checkpoints with automatic escalation, full agent context delivered to the approver.
Audit Trail Application logs only. Not structured for regulatory or compliance export. Immutable, structured audit log on every action — decision, approval, execution, outcome — exportable on demand.
Outcome Feedback Loop Outputs are terminal. What happens after the agent acts is not captured or returned. Structured outcomes returned to AI in every cycle. Agents improve from operational reality.
Deployment Time Platform access immediate. Governance infrastructure: 6–18 months of custom build. First governed agent workflow live in as little as 4 weeks. Enterprise infrastructure included, not built.
Target Buyer AI/ML engineering teams building internal AI capabilities. Operations, procurement, finance, and compliance leaders deploying AI into real enterprise workflows.
Trusted by 100+ Customers

The organisations already running
AI agents on Hubler.

★★★★★

"We had GPT-4 generating our reorder recommendations for six months. The models were accurate — but we had no way to put them into procurement without rebuilding our entire approval chain. Hubler gave us that in four weeks."

VP
VP Procurement
Mid-market retail chain, 200+ stores
★★★★★

"Our compliance team spent three weeks per quarter manually preparing audit packs. Now Hubler produces a complete, structured record of every AI action, human approval, and outcome — in minutes. That time went back to actual compliance work."

GC
Group Compliance Head
Large manufacturing enterprise
★★★★★

"The question our board kept asking was: if the AI makes a decision that goes wrong, who is accountable? With Hubler, we can answer that question completely — every decision, every approver, every outcome, traceable."

CF
CFO
Enterprise distribution company
Questions

What enterprise teams ask
before deploying AI agents.

Does Hubler replace our AI platform or model provider?
No. Hubler is not a model or an agent framework — it is the governance and execution layer that sits between your AI and your enterprise systems. You continue using GPT-4, Claude, LangChain, or any other model or framework you have invested in. Hubler receives structured output from your AI, applies your enterprise governance rules, and executes the resulting actions in your systems. Your AI gets better; your enterprise gets accountable.
How do AI agents connect to Hubler?
Any AI model or agent can connect via Hubler's REST API or webhook. Agents POST a structured execution request — action type, parameters, context, and rationale — and Hubler handles everything from policy check to execution to outcome return. There is no proprietary SDK to adopt. If your agent can make an HTTP request, it can operate on Hubler.
Can we configure which agent actions require human approval?
Yes — at a granular level. You define which action types, value thresholds, or context conditions trigger a human-in-the-loop checkpoint. Low-value, low-risk actions can execute fully autonomously. High-value or regulated actions route to the designated approver with full agent context. You control the trust boundary — and can adjust it as confidence in your AI models grows over time.
What enterprise systems does Hubler connect to?
Hubler ships with pre-built connectors for SAP (ERP, Ariba, S/4HANA), Oracle Financials, Microsoft Dynamics, Workday, Coupa, Jaggaer, and 50+ additional enterprise systems. Connectors are maintained by Hubler — you do not need to build or maintain them. Custom systems and internal APIs are supported via a configurable REST connector that requires no engineering from your team for standard integration patterns.
What happens to the audit trail if we switch AI models?
The audit trail is maintained in Hubler, not in your AI model. It records what execution request arrived, what policy checks ran, who approved what, what system actions executed, and what outcomes occurred — regardless of which model produced the original recommendation. Switching AI models does not affect the integrity or continuity of your audit record.
How does Hubler handle situations where the AI recommendation is wrong?
Human-in-the-loop checkpoints are the primary control. Approvers can modify, reject, or override any AI recommendation before it executes. Policy gates prevent execution of actions that would breach defined rules, regardless of what the AI recommends. And when incorrect AI actions occur despite these controls, the complete audit trail documents what was recommended, what was approved, and why — providing accountability and data to improve the model's next cycle.
Is the audit trail suitable for regulatory submissions?
Yes. The Hubler audit log is structured, immutable, and exportable in standard formats. It records every event with timestamps, actor identity, action context, and outcome — the elements required by most regulatory frameworks for AI-assisted decision accountability. For enterprises in regulated industries (financial services, pharmaceuticals, regulated manufacturing), Hubler's compliance team can assist in mapping the audit record structure to your specific regulatory requirements. The audit trail is yours to own and export at any time.
How long does it take to deploy Hubler for an AI agent use case?
Four weeks for the first governed agent workflow. Week 1: discovery — map the agent use case, policy requirements, and system integrations needed. Week 2: configure — build the governance rules, approval chains, and system connectors. Week 3: train — enable your operations, compliance, or finance teams on the platform. Week 4: launch — first governed AI agent workflow running in production. Timeline may vary based on integration complexity and internal security review processes. Subsequent use cases deploy faster as the foundational infrastructure is in place.
Deploy AI Agents with Confidence

Which AI agent are you ready
to deploy?

Select your first deployment domain. We will walk you through the prebuilt agent, governance rules, enterprise connectors, and audit infrastructure for that domain — in 30 minutes.

Book a 30-Minute Demo → No commitment · Live platform walkthrough · Your use case Talk to a platform specialist first Strategy conversation before you commit to a demo
ISO 9001 & ISO 27001 certified
100+ customers across retail, manufacturing, distribution and services
First agent workflow live in as little as 4 weeks
Works with any AI model or agent framework