Deploy the agents.
Keep the control.
Kaelor is the unified governance layer for AI agents doing consequential work. One policy, compiled into deterministic runtime controls and enforced on every agent, every time, across every model and platform your organization runs. Every decision leaves an independent record: what the agent attempted, which rule applied, and why it was allowed, blocked, or escalated.
Provisional credit is required after 7 business days. This claim is on day 12 with no credit issued.
Reg E · 1005.11(c)(2)
Every agent you add is another copy of your policy to maintain.
Teaching an agent your procedures means configuring it on whatever terms its platform offers. Do that six times across three platforms and you have six interpretations of the same rule and no way to check whether they agree. Then a procedure changes and you do all of it again. The one that quietly gets missed is the one that shows up in an exam.
places the same rule can drift, with nothing comparing them
rule, approved once, enforced on every agent on every platform
One place to change a rule. One record of every decision across all of them.
Your policy becomes the rules. A person approves every one.
Upload the procedure your team already maintains. Kaelor breaks it into discrete rules, each one shown beside the clause it came from. Nothing is enforced until someone signs off, and that approval becomes part of the audit trail from day one.
Frameworks aren't your policy
Most tooling maps agent behaviour to NIST, OWASP or the EU AI Act. You aren't examined against a framework. You're examined against your own stated obligations.
Hand-written rules drift
The alternative is someone typing every rule into a console and maintaining it as procedures change. It separates from the document within a quarter and nobody notices.
Advisory isn't a control
A check the model can choose to call is a check it can skip. You cannot tell an examiner that controls work as long as the model remembers to use them.
Not every rule is a number. Only one tier gets to guess.
Payments compliance is mostly deadlines, thresholds, and codes, which a policy engine can evaluate directly. The rest gets classified first. The genuinely grey cases don't get a verdict at all.
Deadlines, thresholds, codes, counts
A policy engine evaluates structured fields already present in the action. Same input, same verdict, every time.
Unstructured content, still a deterministic verdict
A model reads the content and reports a structured fact, not a judgment: that a message makes a fee claim, not that it's non-compliant. The policy engine decides on that fact. The classification is logged with the model version and confidence, and any historical decision can be reproduced exactly.
Genuinely grey cases don't get a verdict
When a rule is judgment-based, confidence is low, or two rules conflict, the verdict is HOLD, not allow or deny. The action is blocked and routed to a person with the citation and the reasoning attached.
Kaelor doesn't guess on the grey cases. It makes sure they reach a person before the action executes.
Every action your agents took. Proof nothing was edited afterward.
Each row records which tier produced the decision: mechanical, model-assisted, or escalated to a person. A retroactive edit is detectable the moment it happens.
| Time | Action | Verdict | Tier | Basis |
|---|---|---|---|---|
| 14:22:07 | close_dispute | DENY | 1 | REG-E-PROV-CREDIT-001 |
| 14:31:44 | send_notice | HOLD | 3 | UDAAP-DISCLOSURE-004 · conf 0.61 |
| 14:33:12 | send_notice | ALLOW | 3 | released by M. Reyes |
| 15:02:19 | clear_alert | DENY | 2 | BSA-STRUCT-011 · classified: structuring |
This is the audit trail: every action your agents took, which policy version was live at the time, who approved it, and proof nothing was edited afterward.
The agents doing the most already carry the most risk.
High volume, repetitive, and expensive is exactly why a task gets automated first. It's also exactly where a policy mistake compounds fastest.
Customer support
Agents answer questions about pricing, refunds, and what's covered, at a volume no team could staff for. A wrong answer about what something costs is a promise the company now has to honor.
Disclosure rules
IT operations
Agents provision infrastructure, deploy changes, and restart services on their own schedule. A change pushed outside an approved window is an outage, not a bug report.
Approval chains
Dispute resolution
Agents intake disputes, request evidence, and decide whether to credit or deny. An investigation that runs past the deadline makes a provisional credit mandatory, whether the agent knows that rule exists or not.
Provisional credit deadlines
Patient records requests
Agents process requests for medical records and route disclosures to the right recipient. HIPAA gives you thirty days to respond, and sending the right record to the wrong person doesn't get a second chance.
Minimum necessary rule
Claims processing
Agents review claims, request documentation, and decide to approve or deny. Most states require an insurer to acknowledge and act on a claim within a set number of days, and a denial without the required notice is a violation on its own.
State prompt-payment rules
Two ways in. The enforcement doesn't change either way.
Not every agent gives Kaelor the same point of contact, and pretending otherwise would be dishonest. There are two control surfaces, and coverage holds on both.
In the execution path
For agents the customer builds or runs themselves (Claude with MCP, Copilot Studio, LangGraph, in-house), Kaelor sits in the tool-execution path. Preventive. Nothing gets around it.
At the customer's API boundary
A third-party agent's reasoning happens in the vendor's cloud, out of reach. But when it does something consequential, it has to reach into the customer's systems to issue the payment, write to the system of record, or touch the data. That call crosses a boundary the customer owns, and Kaelor governs it.
Kaelor governs consequential actions against your systems, every agent, every time, regardless of which agent, vendor, or model proposed them.
Your policy documents don't have to leave your environment.
Deployed inside your tenant
Kaelor runs in your own cloud. Policy text, agent actions and decision history stay where your security review already put them. The model provider can be one you've already approved.
Fail-closed, with a break-glass
If the check can't run, consequential actions are held rather than released. Fail behaviour is configurable per risk class, and the override is named, logged and yours.
Scoped to what matters
You classify which tools are consequential. Reads, lookups and drafting run untouched with no added latency. Only writes, payments, disclosures and exports are evaluated.
Isolated per organisation
Your compiled policy is never evaluated alongside another organisation's. No shared state, no cross-tenant exposure.
If you're putting agents into regulated workflows, I'd like to hear how you're handling it.
Common questions.
Do we have to change how our agents are built?
No. Kaelor sits in front of the actions you designate as consequential, wherever those get dispatched. For teams running their own orchestration that's a decorator or a framework callback: a single line above the functions that matter.
Is this another dashboard my team has to watch?
No. Enforcement is invisible in normal operation: clean actions pass silently with no perceptible delay. The only interfaces are the review queue, where rules get approved once per policy change, and the audit export.
What happens when a rule doesn't cleanly apply?
The action is held rather than allowed, a review task is created, and the case is logged with the retrieved policy context and the reasoning. Ambiguity routes to a person, and those cases feed the next compilation pass.
Does a model ever decide whether something is allowed?
No. A model classifies unstructured content into a structured fact, for example that a message makes a fee claim. The policy engine decides based on that fact. When a rule is genuinely judgment-based or confidence is low, the action is held for a person instead of decided by either the model or the engine.
Won't my model provider ship this eventually?
They may ship features that help an agent follow an uploaded document. That's different from an independent record you can hand an examiner. An agent that attests to its own compliance is a self-attestation, and a provider feature only ever governs that provider's agents, not every agent you run.
How does this relate to “know your agent”?
Know your agent tells you which agent acted. Kaelor tells you whether it was allowed to. Agent identity answers who, Kaelor answers whether, and the two work together: identity establishes which agent made a request, and Kaelor decides whether your policy permits it and records the outcome.
How do we know the rules were extracted correctly?
Because someone on your team confirmed each one. Every extracted rule appears beside the verbatim clause it came from, and nothing is enforced until a compliance officer approves it. That approval is part of the record.
What does an examiner actually see?
A signed, exportable bundle covering any date range: each decision, the rule that fired, the source clause, the policy version in force at the time, and proof that none of it was edited after the fact.