Products Custom Ai Agent

Custom Agent for policy-aligned workflows built around your business

Create an agent tailored to your process and data model while keeping governance and traceability.

WORKFLOW
Build Custom Agents aligned to your policies, permissions, and workflows
Running
Stockout alertPolicy #09
Overdue escalationPolicy #14
Contract exceptionAuto-routed
1
Evaluate
2
Route
3
Approve
4
Sync
SLA Permission guard Audit trail
Trusted by teams who can't afford revenue leakage

Custom Agent designed around your own operating model

Most teams do not fail because they lack automation ideas. They fail because generic automation does not match their approval rules, ownership model, and exception handling. Sanka Custom Agent is designed to let you define how the agent should reason, what it can execute, and where human approvals must stay in the loop.

A
Policy-aligned behavior

Define guardrails by role, workflow state, and business impact so the agent follows your operating policy by default.

B
Domain-specific prompts and playbooks

Customize prompts and action playbooks for your workflows, such as quote approvals, payment exceptions, and close readiness checks.

C
Traceable execution

Every suggestion and action is logged with user context, timestamps, and record links so operations stay auditable.

What to customize first

Start from the highest-friction handoffs instead of trying to automate everything at once.

  • Define the first operating scope: revenue handoff, procurement exceptions, or close workflows
  • Specify the records the agent may read and the fields it may update
  • Define escalation rules for high-impact changes
  • Set SLA-oriented outputs (owner, due date, and next action)
Customization layer What to define Example
Intent layer Which requests the agent handles "Why is invoice INV-2043 overdue?"
Decision layer Required checks before proposing actions Verify status, owner, due date, and approval state
Action layer Allowed actions by role Create task, route approval, update non-financial fields
Governance layer Mandatory review and audit requirements Finance manager approval for credit adjustments

Example Custom Agent playbooks

Use a Custom Agent as a workflow orchestrator that triages, explains, and routes action with governance.

D
Revenue exception triage

Detect stalled deals-to-order handoffs, summarize blockers, and route owners with due dates.

E
Procurement control checks

Identify PO, receiving, and bill mismatches, then route corrections and approvals by policy.

F
Close readiness operations

Flag missing reconciliations, pending approvals, and unresolved anomalies before month-end close.

Governance first, automation second

Custom behavior should increase speed without creating hidden risk. Keep permissions, approvals, and accountability explicit.

G
Role-scoped permissions

The agent can only act within the same boundaries as the signed-in user and workspace policy.

H
Approval checkpoints

High-impact changes always route through documented approval paths before execution.

I
Auditable event trail

Decisions, prompts, actions, and outcomes are recorded for review, learning, and audit response.

Build iteratively with measurable checkpoints

Treat Custom Agent rollout as an operational program.

  • Phase 1: one workflow, one owner group, one clear success metric
  • Phase 2: expand to adjacent workflows and exception queues
  • Phase 3: standardize templates so teams can launch additional agents faster
Rollout phase Primary KPI Practical checkpoint
Pilot Time-to-resolution Fewer manual follow-ups per exception
Expansion Throughput with controls More exceptions resolved without policy violations
Standardization Reusability Faster launch time for new workflow playbooks

Frequently asked questions

Do we need engineering support to launch a Custom Agent?
Most teams start with workflow definitions and governance rules first, then implement iteratively. Engineering support helps when custom integrations or complex logic are required.
Can we restrict actions to suggestion-only mode?
Yes. Many teams begin with recommendation and routing mode, then enable execution only after approval and audit requirements are validated.
How is this different from a generic AI assistant?
A Custom Agent is configured for your data model, your operating rules, and your escalation logic, so answers and actions stay operationally relevant and controlled.