Trusted AI agents.
Trusted AI agents are not chatbots. They are source-grounded systems that complete work against verified records, cite their evidence, respect privacy boundaries, and leave an audit trail. This is where adoption either earns trust or loses it — and where Prior Signal separates itself from AI hype.
What makes an agent trusted.
Grounded
Retrieval is tied to your own sources (RAG). The agent works from the record, not from memory it can’t show you.
Cited
Every output traces back to the document behind it. No claim without a reference; nothing that can’t be re-examined.
Bounded
Explicit prohibited uses and human-review points are designed in. The agent knows where it stops.
Governed
Audit trails, evaluation harnesses, and monitoring make behaviour visible and correctable over time.
In-jurisdiction
Model-agnostic and self-hostable, so the work can run on Canadian-resident infrastructure when residency matters.
Where agents belong — and where they don’t.
- Intake, triage, and routing
- Document and contract review
- FOI / ATIP drafting and returns analysis
- Policy, regulatory, and filing monitoring
- Competitive and market signal tracking
- Customer and case operations support
- Final legal advice
- Medical diagnosis
- Benefit or eligibility denials
- Unsupervised decisions on a person’s rights
- Anything that cannot be checked against a source
How Prior Signal builds them.
We wire agents to your actual systems through the Model Context Protocol, ground them in your sources with retrieval and citation tooling, and put human-review points where decisions carry weight. Every system ships with an evaluation harness and monitoring, so behaviour stays visible and correctable.
We work across the leading frontier models — Anthropic’s Claude, OpenAI, and others — plus self-hostable open models, staying model-agnostic. Built in-house, governed, and yours to keep. See the full capability set or our approach to AI governance.
Common questions.
What makes an AI agent “trusted”?
- A trusted agent completes multi-step work against verified records, cites the evidence behind each step, respects privacy and access boundaries, and leaves an audit trail you can review. It is grounded, bounded, and governed — not a black-box chatbot.
How is this different from a chatbot?
- A chatbot generates text. An agent does work — retrieving records, comparing them, drafting outputs, and taking defined actions through connected tools (via the Model Context Protocol) — with every output grounded in your sources and checkable against the original.
Where should AI agents not be used?
- Final legal advice, medical diagnosis, benefit or eligibility denials, and any unsupervised decision about a person’s rights or entitlements. Agents support those workflows with grounded research and drafting; a qualified human owns the decision.
Can agents run on Canadian-resident or self-hosted infrastructure?
- Yes. We are model-agnostic and can deploy on self-hostable open models and Canadian-resident infrastructure for sovereignty-sensitive work, so data residency and operational control stay with you.
Adopt AI without losing control of the record.
Start with a scoped assessment of where agents fit, where they don’t, and what has to stay checkable.