Control layer for production AI systems

AI agents that act.
Under your control.

AgenticCAP sits between an AI agent's decision and your real-world systems. Add evidence, policies, human approvals and complete audit trails without rebuilding the application around governance.

Explore the SDK → See how it works
EvidencePoliciesApprovalsActionsAudit
live action trace FERRI · TRANSPORT trc_9f3a…
AI AGENT REQUEST
assign_vehicle_and_driver
tenant: ferri · environment: production
✓
Evidence capturedcontract · planner result · driver state
stored
✓
Policy evaluatedferri-driver-assignment-v1
matched
!
Human approval requiredoperator approval before execution
•
Execute actionwaiting for authorization
pending
Every step traceable from intent to execution.

The same control layer can govern a real transport action.

Know whyLink decisions to evidence.
Control whatEnforce rules before action.
Approve when neededKeep people in the loop.
Prove what happenedMaintain a complete audit trail.
FROM ANSWERS TO ACTIONS

When AI can act, observability is not enough.

AI assistants answer questions. AI agents can create records, assign resources, send documents, trigger APIs and change business systems. AgenticCAP controls the critical space between a model decision and the resulting action.

01 Agent Intent + proposed action
→
02 Evidence Inputs + context + outputs
→
03 Policy Allow · approve · deny
→
04 Approval Human when required
→
05 Action Authorized execution
⌁

Trace every workflow

Follow an AI operation from request to evidence, decision, approval and final action.

◇

Capture evidence

Store the inputs, outputs and snapshots that explain why an AI system reached a decision.

⌘

Enforce policy

Define which actions are allowed, blocked or require approval per tenant, agent or workflow.

✓

Keep humans in control

Route sensitive operations to the right person before anything changes in production.

↗

Authorize real actions

Gate operations against your APIs, TMS, ERP, CRM, documents, messaging and internal services.

▦

Audit across tenants

Separate credentials, policies and audit history while keeping one reusable control platform.

ONE PLATFORM. VERY DIFFERENT PROBLEMS.

Same control model. Different industries.

AgenticCAP stays horizontal. Your application keeps the domain logic.

PUBLIC ADMINISTRATION Knowledge workflow

Turn hours of meetings into verifiable context.

“What did the mayor say about waste collection, and how did councillors respond?”

Video→Transcription→OCR→Context
  • Trace transcription, OCR and contextual analysis
  • Link answers to source segments and documents
  • Preserve the evidence behind generated conclusions
Used as a control layer for document and meeting intelligence.
TRANSPORT & LOGISTICS Action workflow

Turn an operational request into a controlled action.

“Create tomorrow's transport, find the best vehicle and driver, and send the documentation.”

Chat→Planner→Policy→Execute
  • Capture contract, planner and driver evidence
  • Require approval before production changes
  • Audit TMS actions and driver communication
Used as the governance layer around real transport operations.
INTEGRATE ONCE

One SDK. Any workflow.

Your application owns the business logic. AgenticCAP owns the control lifecycle around it. Integrate the SDK once, then evolve tenant policies and approvals without rewriting the agent.

01

Your domain stays yours.
AgenticCAP does not need to understand a truck, a mayor, an invoice or a support ticket.

02

Governance stays centralized.
Change an action from allow to approval without redeploying the whole application.

03

Multi-tenant by design.
Use the same platform and SDK with separate policies, credentials and audit histories.

agent_workflow.py Python 
from agenticcap import CAPClient

cap = CAPClient(
    tenant_id="ferri",
    api_key=AGENTICCAP_API_KEY,
)

trace = cap.traces.create(
    agent_id="operations-copilot",
    action="assign_vehicle_and_driver",
)

cap.evidence.submit(
    trace_id=trace.id,
    payload=context_snapshot,
)

decision = cap.policies.evaluate(
    trace_id=trace.id,
    action="assign_vehicle_and_driver",
)

if decision.requires_approval:
    cap.approvals.request(trace_id=trace.id)

cap.traces.finish(trace.id)
SDK Trace · Evidence · Policy · Approval · Audit
CONTROL WITHOUT REBUILDING YOUR AGENT

Change governance, not application code.

One action can behave differently for each tenant, environment or risk level.

Tenant Aassign_vehicleALLOW
Tenant Bassign_vehicleREQUIRE_APPROVAL
Critical actioncancel_transportDENY
KEEP YOUR STACK

AgenticCAP doesn't replace your AI or your systems.

It becomes the control layer between them.

YOUR AIAgent / Copilot
SDK
CONTROL LAYERAgenticCAP
AUTHORIZED ACTIONS
YOUR SYSTEMSAPIs · ERP · CRM · TMS · Documents · Messaging
!

An AI answer can be wrong.
An AI action can be expensive.

As AI moves from generating content to executing business processes, teams need to know what the agent saw, what it decided, which rules applied, who approved the action and what was actually executed.

That is the problem AgenticCAP is built to solve.
BUILD CONTROLLED AI WORKFLOWS

Ready to let AI do more than answer questions?

Bring your agent. Bring your workflow. AgenticCAP provides the control layer.

Build with AgenticCAP → Talk to us