AI Agents for Business
Claravance Agentic Workforce - AI Agents That Arrive Ready to Work
Claravance Agentic Workforce provides pre-built, governed AI agents for high-volume digital work - request triage, data entry and enrichment, document processing, and routine service tasks. Each agent deploys with bounded permissions, audit logging, and human escalation, configured to your process rather than coded from scratch.
The Agent Catalog
Custom agent development is the right answer when a workflow is genuinely unique to your business. But a great deal of the repetitive digital work in any company follows the same handful of shapes: something arrives, it's classified, data is extracted, a system is updated, an exception is escalated. Agentic Workforce productizes those shapes. You configure a proven agent role instead of commissioning a build - which is the difference between weeks and quarters.
Triage Agent
Classifies and routes incoming requests, tickets, or emails to the right queue with the right priority.
Data Entry & Enrichment Agent
Transfers and enriches records between systems, ending the copy-paste-between-screens tax.
Document Processing Agent
Extracts structured data from invoices, forms, and reports into your systems with confidence scoring.
Service Request Agent
Resolves routine internal requests end to end, including the system updates they require.
Governance Is Built In, Not Bolted On
Because agents act, governance isn't optional. Every agent in the platform ships with:
Bounded permissions. An explicit action space - what it may do, what needs approval, what it can never touch. If you can't state an agent's permissions in a sentence, it's misconfigured.
Human escalation. Confidence thresholds and review queues, so exceptions reach people rather than getting quietly guessed at.
Full audit trails. Every action logged and reviewable - for your own oversight and for whoever eventually audits you.
Products vs. Custom Agents
| Choose Agentic Workforce when… | Choose custom agents when… |
|---|---|
The task follows a common pattern | The workflow is proprietary or a competitive advantage |
You want value in weeks | Deep integration with unusual systems is required |
IT capacity is limited | You need full control of logic and models |
You'd rather subscribe than own a codebase | The process spans many systems with complex exceptions |
Deployment and Security
Live in two days, connected to the systems the agent reads from and writes to
SSO, role-based access control, audit logging, and data-residency options
Your data is never used to train models shared with other customers
Configuring an Agent to Your Process
Pre-built doesn't mean generic. Each catalog agent is configured against your actual workflow, and these are the configuration decisions that matter:
Inputs and triggers. What starts the agent - an inbound email, a queue item, a scheduled run, a webhook.
Classification rules. Your categories, your priorities, your routing logic. This is where most of the value is created, and it's your domain knowledge rather than ours.
Action permissions. Which systems the agent may read from and write to, and which actions require approval. Configured per agent and enforced by access control.
Escalation criteria. The confidence threshold and the specific case types that always go to a person regardless of confidence - complaints, legal matters, high-value exceptions.
Review and audit. Who reviews escalations, who reviews samples of autonomous work, and who receives the audit log.
Configuration typically takes two days, with your process owner involved throughout. The process owner's involvement is not optional - an agent configured without the person who actually knows the exceptions will handle the common cases well and the important ones badly.
Find out which of your processes an agent could own.
Describe two repetitive processes; we'll tell you which is a fit today and what it would save.
The Exception Question
Every process has undocumented exceptions, usually handled by whoever has been there longest. In our experience these account for a meaningful share of volume - commonly twenty to forty percent - and they are almost never in the process documentation. An honest agent deployment plans for this rather than discovering it:
Surface the exceptions during configuration by interviewing the people who handle them, not by reading the procedure
Route them deliberately - the agent recognizes and escalates rather than guessing
Track escalation patterns, because recurring escalations are either configuration gaps to close or genuinely human work to leave alone
Expand coverage over time as patterns become clear
Agents that promise full automation on day one either haven't found the exceptions or intend to guess at them. The second is worse.
Governance Evidence You Can Hand to an Auditor
Because agents act on your systems, oversight has to be demonstrable rather than asserted. What the platform produces: a complete action log per agent with timestamps, inputs, decisions, and outcomes; permission configuration history showing who changed an agent's authority and when; escalation records with resolution; and performance reporting against the accuracy baseline established at deployment.
This matters for internal oversight, and increasingly for external obligations - AI-specific regulation is arriving, and audit trails are a recurring requirement across frameworks. Our AI governance practice can map your agent deployments against the regimes that apply to you.
Frequently Asked Questions
By surfacing undocumented exceptions during configuration through interviews with the people who currently handle them, then routing those cases to humans deliberately rather than letting the agent guess. Escalation patterns are tracked so coverage can expand as gaps become clear.
A complete per-agent action log with inputs, decisions, and outcomes; permission change history; escalation records with resolutions; and accuracy reporting against the deployment baseline - the evidence internal oversight and emerging AI regulation both require.
AI agents are software systems that complete multi-step tasks - reading inputs, gathering context from business systems, taking permitted actions, and escalating exceptions - rather than simply generating a response. They suit high-volume, rule-governed digital work.
RPA follows fixed scripts and breaks when formats or screens change. Agents interpret unstructured inputs and adapt, but require guardrails, evaluation, and audit logging that RPA doesn't. Agentic Workforce provides those controls as part of the product.
Bounded permissions that define exactly what it may touch, approval requirements on consequential actions, confidence thresholds that force escalation rather than a guess, and complete audit logging of every action taken.
Two days for a catalog agent configured to your process, versus the eight to sixteen weeks a custom agent build typically takes.
In practice they absorb the repetitive share of the work and route judgment cases to people. Teams generally redeploy capacity to exception handling and higher-value work - and we'd rather discuss that openly during scoping than let it surface as a surprise during rollout.