The only way for a company to become AI-native, is with AI-native processes. Tonkean enables ops teams to architect org-wide processes that operationalize agents. This means transforming agent usage from personal productivity to enterprise standard.
Operationalize AI by layering agent orchestration, deterministic process logic, and a context graph between any front door and any integration — the engine that turns raw connectivity into reliable autonomous execution.
LLM / Chat Interface
(sometimes with addition of a task agent)
MCP connection to system(s)
Why this falls short
× No process accountability — compliance, visibility, and quality are unmanaged
× Weak execution quality due to missing business context
× No autonomy — each step requires manual orchestration
Key Insight
A unified interface reduces software friction — but enterprise friction also stems from how each organization operates. Completing a task requires knowing which system to use, when, what's permitted, and who must approve. Connecting an API or MCP endpoint to an LLM does not solve this. Reliable execution requires a data context layer that understands business terminology and policies, and a process layer that encodes what must happen when a task is triggered.
Why this is hard
The integration challenge is not connecting to a system — it is adapting to each customer's environment. Not JIRA in the abstract, but a specific instance with its own workflows, permissions, and operational context.
Any LLM, chat interface, agent, or protocol can serve as a front door to Tonkean. Agents can be invoked from any source — other agents, MCP clients, messaging platforms, APIs, or scheduled triggers — and return rich, rendered views back to the client.
Key Insight
One agent, many front doors — any system, protocol, or user can invoke a Tonkean agent and receive rich, contextual responses without custom integration work per channel.
Why this is hard
Most agent platforms support a single invocation path. Supporting heterogeneous triggers — A2A protocol, MCP, messaging APIs, webhooks, email parsing, scheduled jobs — with unified auth, rate limiting, and context injection is a serious infrastructure challenge.
Invocation & Response Flow
Tonkean Orchestrator Agent
Processes & responds
Rendered View
Text / UI returned to client
Rendered View Example — Slack Chat
Budget Information
Orchestration Topology
Orchestrator
Orchestrator Agent
Tonkean agents are Subject Matter Experts — each owns a specific domain, carries institutional knowledge, and operates with defined responsibilities. They don't call each other directly — they call the orchestrator, which brokers every interaction with unified auth, policy, and context.
Key Insight
Just as organizations rely on subject matter experts — procurement specialists, legal reviewers, IT analysts — Tonkean agents are purpose-built for specific domains. Each is onboarded with defined responsibilities, domain knowledge, and operating procedures, then governed through a central orchestrator.
Why this is hard
Onboarding an agent into an organization requires encoding institutional knowledge — process definitions, exception paths, approval hierarchies, and domain-specific terminology — then validating behavior against real scenarios. Most platforms skip this entirely and ship a prompt with API access.
A chat interface removes the friction of navigating software — but enterprise tasks also require process knowledge: which system to use, what's allowed, who approves. These flows show how orchestration handles the full task, not just the front door.
A request agent invokes policy and Amazon Business agents to source compliant options, then routes through risk and approval before executing in Coupa. Connector access alone wasn't enough — without an orchestration engine to apply process logic and business context, the data couldn't drive autonomous execution.
Request Agent
Intake
Policy Agent
Budget & Rules
Amazon Business
Sourcing
Risk & Compliance
Validation
Approval Agent
Human-in-the-Loop
Research Coupa PO
Tonkean Agent
| Agent | Field | Value |
|---|---|---|
| ERP (SAP) | Budget remaining | $42,300 |
| Amazon Business | Best price | $1,250/unit |
| Coupa | Preferred vendor | Acme Corp |
| Approval History | Last approved by | J. Smith (VP) |
| Human Input | Requester note | "Urgent — Q3 deadline" |
| Past Learning | Similar requests | 87% chose Acme |
A monitoring agent detects anomalies and invokes parallel diagnosis and log analysis agents, then routes through remediation, human escalation, and postmortem — closing the loop with knowledge base updates.
Monitoring Agent
Anomaly Detection
Diagnosis Agent
Root Cause
Log Analysis Agent
Pattern Match
Remediation Agent
Auto-fix
Human Agent
Escalation
Postmortem Agent
Knowledge Base
| Agent | Field | Value |
|---|---|---|
| Datadog | Alert severity | P1 — Critical |
| ServiceNow | Prior incidents | 3 similar (90d) |
| CMDB | Affected service | payments-api |
| Slack | On-call engineer | M. Chen |
| Human Input | Engineer override | "Skip canary deploy" |
| Past Learning | Prior resolution | DB restart (92%) |
An invoice agent captures incoming invoices, validates against PO and budget agents, routes through compliance and approval, then triggers payment execution in the ERP.
Invoice Agent
Capture & Parse
PO Matching Agent
3-Way Match
Budget Agent
Allocation Check
Compliance Agent
Audit & Policy
Approval Agent
Human-in-the-Loop
Payment Agent
ERP Execution
| Agent | Field | Value |
|---|---|---|
| NetSuite | PO amount | $18,500 |
| Invoice (OCR) | Billed amount | $18,750 |
| GL System | Dept. budget left | $62,100 |
| Audit Trail | Last approver | R. Patel (Dir.) |
| Human Input | CFO comment | "Hold >$15k invoices" |
| Past Learning | Match success rate | 94% auto-matched |
A person asks to create an NDA. Without Tonkean, an LLM with MCP access might create a blank doc. With Tonkean, an SME agent knows the contracts folder, selects the correct template, populates terms from the CRM, and routes through DocuSign — governed by the organization's approval logic.
Contract Request Agent
Intake & Intent
Template Agent
Selects NDA template from contracts folder
CRM Agent
Populates deal terms
Legal Review Agent
Policy & Redline
Approval Agent
Human-in-the-Loop
DocuSign Agent
E-signature execution
| Agent | Field | Value |
|---|---|---|
| Google Drive | Contracts folder | /Legal/Templates/NDA_v4 |
| CRM (Salesforce) | Deal value | $340,000 |
| Legal Playbook | Approval threshold | >$100k requires GC |
| DocuSign | Routing | Counterparty → Legal → CEO |
| Human Input | Legal counsel | "Cap indemnity at 1x" |
| Past Learning | Similar NDAs | Avg 2.3 rounds |