ARCHER EVOLV™ AI OPERATORS
Governed AI, doing real GRC work.
An AI Operator is a governed agent built for one real job inside Archer: reading regulatory text, scoring a vendor, drafting a finding, watching for the next disruption. Every operator is identity-bound, scope-constrained, and audit-emitting.
9
GRC domains
60+
Operators on the roadmap
1
Shared runtime
5
Stage adaptive loop
THE CHALLENGE
Most AI stops at drafting. Governance needs more than that.
MOST AI STOPS SHORT
Useful isn't the same as defensible.
Enterprises are rapidly introducing AI into GRC programs, but most deployments stop at summarization, drafting, or search. They reduce effort without reducing risk. It's useful. It isn't defensible.
THE AUDIT-TRAIL PROBLEM
A log file isn't an audit trail.
Regulators expect a documented, auditable record of what an AI concluded, how it got there, and what trail it left behind. A log added after the fact doesn't meet that bar.
GENERIC MODELS FALL SHORT
Generic LLMs weren't built for GRC.
Generic large language models run 26% to 94% error rates on domain-specific compliance queries. Most AI running inside GRC programs today can't clear the bar regulators now expect.
THE CONCEPT
Not a catalog of features. A teammate that follows one template, every time.
Read as a list, AI Operators can sound like sixty unrelated point solutions. They're not. Every digital worker is built to the same pattern and inherits the same guardrails, which is what makes it possible to trust the next one before you've even seen it work.
An AI Operator is a member of your ecosystem, not a second console. Controls move left into the build pipeline, compliance-as-code, so they're checked before a workload ships instead of after audit finds the gap. The same operators serve second line (risk) and third line (audit) functions alike.
Explainable logic
Every decision carries its reasoning, so nothing is a black box.
Actions stay inside your approval gates, never around them.
Expert-in-control
Auditable actions
Full lineage on every change, from trigger to write-back.
WHAT'S LIVE TODAY
Not a roadmap slide. Operators running now.
Domain Onboarding
Maps processes to risks to controls for any domain, aligned to your own records.
Control generation & verification
Drafts controls, checks coverage, and flags gaps against the risk they address.
Guardrail conformance
Tests AI guardrails against controls and codified regulation.
Violation & incident reporting
Thematic search across incidents, linked back to the record in Archer.
Controls assurance
Judges whether a control is effective against its risk, with cadence and evidence checks.
Business asset hygiene
Finds duplicate, orphaned, or low-quality records.
THE ADAPTIVE LOOP
The adaptive loop that runs the program
The same adaptive loop that runs Archer Evolv Compliance runs here, told in risk terms: Listen, Decide, Act, Assure, Learn.
WHERE OPERATORS LIVE
Nine domains, one system underneath.
Compliance is live today and built the foundation everything else stands on. Every other domain is on the roadmap, instantiating the same loop on the same data. Below is a representative look at each, not the full roster, just enough to see the shape of what each domain does.
WHERE OPERATORS LIVE
One data spine. One runtime. Every domain builds on top.
Compliance ships first because it builds the spine everything else reads from, the authoritative lineage of obligations, controls, business units, risks, and processes inside Archer. Every planned domain operator reuses that spine instead of rebuilding it, and all of them run on the same underlying engine.
The GRC Data Spine: built by Compliance, live today
Clean, connected lineage is what makes every downstream automation trustworthy.
Obligations
Controls
Risks
Processes
Business Units
Run on
AI Operator Framework: the shared runtime
Signals already moving through Archer become governed outcomes on a schedule, inside your approval gates.
Audit
Curate
Research
Route
Report
Orchestrate
Read &
Write
Domain Operators
Planned 2026+
Audit, AI Risk Management, Third Party Risk, Policy Change Management, IT Risk, Operational & Enterprise Risk, Resiliency
WHERE THIS GOES
Every domain climbs the same four stages.
STAGE 1 : FOUNDATION
Core Automation
Proves: time savings
Automate the manual task: roll forward an engagement, extract obligations, build a vendor profile.
STAGE 2 : SCALE
Intelligence
Adds: consistency
Standardize outputs at volume: classification, scoring, and reusable templates remove reviewer variation.
Operators arrive in waves, not all at once, each stage depends on the trust and data the one before it builds.
STAGE 3 : INTELLIGENCE
Risk Insight
Enables: early detection
Shift from reactive to predictive: anomalies and emerging signals surface issues before they become findings.
STAGE 4 : INTEGRATION
GRC Orchestration
Delivers: connected GRC
Operators coordinate across domains: one control failure informs risk, audit, and resilience posture at once.
THE DIFFERENTIATION
Three pillars: the combination no one can replicate.
Built for GRC
Archer's proprietary models are trained specifically on regulatory and GRC data, since 2017. Not adapted from general-purpose productivity tools.
492
PROPRIEYARY MODELS
Contextual intelligence
These digital workers are grounded in the Archer record your organization has already built: controls, policies, findings, and workflows generic AI simply doesn't have access to.
Engineered for governance
Draft-and-recommend only, by design. No silent auto-approval, ever. Every run is evidence-backed and logged.
100%
OF ACTIONS HUMAN- APPROVED BEFORE WRITE-BACK
25+
YEARS AS THE SYSTEM OF RECORD
GENERIC AI AGENTS ARE OUTPACING GOVERNANCE
Impressive in demos. Indefensible in audits.
The agents are useful. The agents are productive. Productivity without governance is exposure. Archer Evolv builds every digital worker inside the control environment, without slowing what it can do.
GENERIC AI AGENT
Impressive in demos. Indefensible in audits.
Foundation model with a prompt and discretion to call tools
No native identity binding or scope constraint
Outputs without calibrated confidence or justification
Locked to one LLM provider
26% to 94% error rates on domain-specific queries
AI OPERATOR
Archer Evolv AI Operators
Identity-bound (SAML/OIDC + SCIM 2.0) and scope-constrained
Every action emits a structured, immutable audit record
Calibrated confidence and justification on every output
Multi-provider model fabric — no LLM vendor lock-in
Expert-in-the-Loop supervision from 130+ regulatory specialists
FREQUENTLY ASKED QUESTION
Questions we hear most often
Evolv
Compliance
Risk Management
Revolutionize Compliance and Risk Management with Archer Evolv™
