Declared scope
Actions and intended outcomes are expressed before evaluation, so the decision boundary is explicit rather than implied.
CONTROLLED AI-AGENT OPERATIONS
Give agents a defined path to act — and a reliable reason to stop.
Agents Align is a research-stage control-plane approach for keeping AI-agent activity inside declared policy, evidence, and authorized scope.
A bounded decision path
- 01DeclareState the proposed action and its intended outcome.
- 02CheckTest the proposal against explicit governing constraints.
- 03VerifyUse evidence and uncertainty as reasons to pause, not to overreach.
- 04RecordRetain a traceable decision receipt for review.
THE APPROACH
Control before capability.
The central question is not only what an agent can do. It is whether a particular action, in a particular context, is justified and within scope.
Evidence-aware restraint
Missing, ambiguous, or conflicting evidence should lead to a halt or review—not a confident guess.
Traceable decisions
Decision receipts make it possible to inspect what was evaluated and why a proposal was constrained.
DESIGN PRINCIPLES
Designed to make uncertainty visible.
Agents Align is being developed around boundaries that resist silent expansion of authority.
01
Constraints come first
Governing rules are a prerequisite to any later assessment; they are not a score an agent can negotiate around.
02
No implicit execution authority
An evaluation path is not an authorization path. Assessments must not quietly become permission to act.
03
Human review remains meaningful
When evidence is insufficient, the system is designed to preserve the decision for accountable review.
RESEARCH STATUS
Early-stage, deliberately conservative.
Agents Align is a research and architecture effort. Its current work demonstrates controlled evaluation and refusal boundaries; it is not presented as a production authorization system or as a system that grants autonomous operational authority.
What has been exploredDeterministic constraint checks, uncertainty halts, and decision receipts.
What remains outside scopeProduction authorization, unbounded tool access, and autonomous external action.
VALIDATION & BENCHMARKS
Tested against pressure to exceed the boundary.
A deterministic three-agent governance corpus tests whether constraints hold when agents compete, proposals conflict, or a request attempts to escape its declared scope.
3distinct agent roles
36labelled benchmark cases
0external effects
0grants issued
What was tested: undeclared external scope, irreversible changes, secret-access attempts, proposed replacement of a bounded action, contradictory or missing evidence, and cross-agent receipt substitution.
What the result means: In this controlled corpus, out-of-scope cases were denied or routed to review; an eligibility receipt from one agent could not be reused by another. The benchmark is research evidence for boundary enforcement, not a security certification or a claim of production readiness.
GUIDED EVALUATION
Try a bounded evaluation.
Choose an illustrative scenario to see how declared scope, evidence, and constraints shape a decision.
This interactive preview uses synthetic, pre-defined scenarios. It makes no network calls, executes no actions, and grants no authority.
Request a guided demoIllustrative evaluationSIMULATED
DECISION
Denied — scope mismatch
The proposed action targets an undeclared external system, so the evaluation stops before any execution path is considered.
- Constraint
- Declared target scope
- Evidence
- Target is not in the proposal
- External effect
- None
- Grant issued
- No
Illustrative receipt AA-DEMO-SCOPE-01
The guided demo is an evaluation experience, not a production authorization service and not an invitation to connect live systems.
PARTNERSHIPS & RESEARCH
Interested in governed agent operations?
We welcome conversations with research partners, enterprise technology teams, and organizations exploring accountable AI-agent deployment.
Contact Agents Align