01
No access to business data
Orders, tickets and inventory are spread across a dozen systems. Public-cloud AI cannot reach that data, and it should not.
Enterprise Agent Platform
Enterprise AI agentsrunning inside your network
KybWork connects your HRM, OA, PMS, CRM, ERP and other business systems, letting agents query, reason, act and audit within your permission boundaries.
- On-premises
- Compatible with major LLMs
- Inherited permissions
- End-to-end audit
Why did support tickets spike this month?
Execution plan
- ticketing.query()Ticketing · 412msTickets are up month over month, mostly from product line A
- procurement.deliveries()Procurement · 538msKey parts for product line A arrived late last month
- project.milestones()Projects · 297msThe "Line A upgrade" delivery milestone slipped as a result
Conclusion
The spike comes from product line A: late parts delayed the upgrade, so issues in the old version piled up.
Assign task to procurement leadNeeds approvalDraft customer messaging
3 tool calls · run as the requesting user · written to audit log
- On-premises
- Supports any model
- Inherits enterprise permissions
- Every call audited end to end
The gap
LLMs are smart, but they cannot get into your business
Enterprises do not need another chat assistant. They need agents that can get real work done in real systems, within real permission constraints.
02
No control over permissions or risk
Generic assistants have no enterprise permission model. They do not know who may see what, or which actions require approval.
03
No way to trace an answer
Answers cannot be traced to a source. When something goes wrong, nobody can tell which call or which data was involved.
Agent loop
Connect, reason, act, audit: a complete agent loop
From existing systems to real business actions, every stage has clear boundaries.
- 01
CONNECT
Connect · tools
Standard connectors publish your systems as business tools with fixed schemas. Field mapping, incremental sync and retries come built in, with no custom integration code per customer.
- 02
REASON
Reason · orchestrate
Agents interpret intent, fill in parameters, plan the call sequence, and connect cause and effect across systems. The knowledge base supplies policy context; business figures always come from live APIs.
- 03
ACT
Act · with guardrails
Tools are called as the requesting user, and only allow-listed business actions are available. Money and HR actions go to human confirmation or an approval chain.
- 04
AUDIT
Audit · observe
Every call records latency, tokens, data sources and results, and can be traced and replayed. Outcome metrics agreed before go-live become acceptance criteria.
Agent roster
A roster of agents configured by business domain
Each agent has a clear domain, a tool allowlist and approval boundaries. Prompts, tools and checkpoints are configuration, not code.
Finance analyst
agent://fin-analyst
ToolsExpensesBudgetsAR/AP
HR assistant
agent://hr-assistant
ToolsEmployee recordsLeaveRecruiting
Legal reviewer
agent://legal-review
ToolsContractsPolicies
Human approval
Service copilot
agent://cs-copilot
ToolsTicketsKnowledge baseCustomers
Procurement agent
agent://procurement
ToolsSuppliersDeliveriesPayments
Amount check
Strategy analyst
agent://strategy
ToolsCross-domain KPIsProjectsFinance
Platform
Complete agent infrastructure, from model to business action
Runtime, tools, orchestration, observability and knowledge share one organization model, one permission model and one set of metric definitions.
Agent runtime
Runs agents for every business domain: intent recognition, parameter completion, tool selection and answer composition, with swappable models.
Tools & connectors
Existing systems are published through standard connectors as fixed-schema business tools. No generic "run any process with any variables" operations.
Orchestration & approvals
Multi-step task orchestration with approval chains on critical actions. Runs can pause and resume, and every step is recorded.
Observability & evaluation
Call volume, success rate, latency and token usage in real time. Failed calls are traced to the exact tool and parameters.
Enterprise knowledge retrieval
Policy and document retrieval provides grounding and citations. Balances, statuses and amounts always come from live APIs.
Business workspaces
HR, administration, projects, finance and service workspaces: a workbench for people and a tool domain for agents.
Comparison
Not another chatbot
| KybWork agent platform | Generic AI assistants | AI in collaboration suites | |
|---|---|---|---|
| Data it can access | All your existing business systems | Whatever users paste in | In-suite documents and messages |
| Permission model | Inherits enterprise permissions per caller | No enterprise permissions | Limited to the suite’s own |
| Can it take business actions | Allow-listed tools + human approval | Suggestions only | Only inside the suite |
| Deployment & data | On-premises, works offline | Usually public cloud | On-prem editions have a high entry bar |
| Auditability | Every call recorded end to end | Hard to trace | Depends on edition |
Comparison reflects typical product shapes in public materials. Actual delivery varies by vendor.
Governance
Control is the first requirement of an enterprise agent
Built for data-sensitive industries such as finance and healthcare. Data, models and logs stay under your control.
Data stays in your network
Business data, knowledge base, inference requests and logs never leave your network boundary.
No privilege escalation
Agents access data as the calling user, never with super-user rights. What you cannot see, the agent cannot see.
High-risk actions need approval
Operations involving money or HR changes must pass human confirmation or an approval chain. Agents cannot approve themselves.
End-to-end audit
Who asked, which tool was called, which data was read and what came back: all recorded and searchable.
Industries
Built for data-sensitive organizations with 1,000+ people
Financial institutions
Agents for expense analysis, compliance review and service QA that meet data sovereignty and audit requirements.
Healthcare providers
Agents for hospital operations management, with data and inference that never leave the hospital network.
Large private enterprises
Cross-business-line analysis and process automation agents, not tied to any ERP.
Rollout
Start with one agent and see a verifiable result in 8 weeks
No big-bang rollout. Pick one scenario, connect three data sources, and agree on one measurable metric.
- 1
W1–2
Connect & check
Connect 3 data sources and deliver a data health report
- 2
W3–4
Define the agent
Configure tool allowlist and approvals; confirm the baseline
- 3
W5–6
Guarded pilot
The agent goes live with human confirmation on key actions
- 4
W7–8
Compare results
Measure run records against the baseline and report
Book a demo
Get your first agent running inside your network
Tell us which systems you run and what work you want to hand to agents first. We will prepare a demo and deployment plan for your environment.
