What Is Know Your Agent (KYA) Means-Testing System?

What Is Know Your Agent (KYA) Means-Testing System?

A Know Your Agent (KYA) means-testing system is an agent governance control that verifies an AI agent’s identity, declared tools, data scope, and authorization before it can act in production. It also checks ongoing behavior against those declarations in traces, eval runs, and gateway decisions. FutureAGI treats KYA as a production reliability layer for A2A and MCP-connected systems, where one agent can invoke another and a missing identity check can become a cross-system permission failure.

Why know-your-agent means-testing matters in production agent systems

Agent stacks introduced a new identity problem: an agent is not a user, not a service, and not a deterministic API client. It can change behavior between invocations, take new actions, and call other agents. Without an identity-and-capability gate, the platform cannot answer basic compliance questions: “what did agent X do yesterday?”, “is agent X allowed to call this tool?”, “did agent X stay within its scope?” The cost of skipping KYA is concrete: a customer-support agent invoking a billing-refund tool it should never have access to, an external partner agent calling internal tools because no one verified its declaration, or an MCP server returning data to the wrong tenant.

Security and compliance teams feel this hardest. Auditors ask “how do you control which agents access PII,” and “we trust the prompt” is not an answer. SREs see incidents where one agent calls another in a loop, neither bounded by capability declarations. Product managers see customer-trust failures when an agent steps outside its lane.

In 2026, A2A protocols and MCP-based tool ecosystems made KYA a structural need, not a nice-to-have. Unlike IAM policy checks or Open Policy Agent rules, KYA tests whether a generative agent behaves like the profile it registered, not only whether a static service principal has permission. The OWASP LLM Top 10 category for excessive agency maps directly to missing-KYA failure modes. The cost of a missing KYA gate compounds across an agent network because agents call agents; a single mis-authorized call can chain into many.

How FutureAGI handles know-your-agent means-testing

FutureAGI’s approach is to make agent identity and capabilities first-class fields on every trace and eval. At trace level, traceAI integrations such as langchain, openai-agents, and mcp emit OpenTelemetry spans with agent.id, agent.version, agent.trajectory.step, and tool.name on every step, so the platform can answer “did this trajectory only call tools the profile declared?” without custom instrumentation. At eval level, fi.evals.TaskCompletion and fi.evals.GoalProgress score whether the agent’s behavior matches its declared task; fi.evals.ActionSafety scores whether actions stay within safe bounds; fi.evals.ToolSelectionAccuracy scores whether the agent picked the right tool from its declared tool set.

At gateway level, the Agent Command Center’s pre-guardrail validates the agent profile on every invocation — agent id, declared tools, scope tags — and can block calls where the runtime profile doesn’t match the registered one. A post-guardrail re-checks the response for scope violations (e.g. a customer-support agent accidentally returning admin data).

Concretely: a platform team registering external partner agents pins each agent’s profile in a Dataset with declared tool list, authorized data scope, and a frozen probe set of expected behaviors. The platform runs a regression eval over the probe set on every onboarding and at scheduled intervals after: TaskCompletion for goal alignment, ActionSafety for scope adherence, ToolSelectionAccuracy for tool-set conformance. When a regression fires, the agent is gated until the team investigates. FutureAGI’s view: KYA is observability, eval, and gateway control, not just a registration form.

How to measure or detect know-your-agent means-testing

KYA means-testing produces both runtime gate signals and ongoing eval signals:

• fi.evals.TaskCompletion — verifies the agent achieves its declared task across the trajectory.
• fi.evals.ActionSafety — scores whether each action stays within the agent’s declared scope.
• fi.evals.ToolSelectionAccuracy — verifies the agent calls only declared tools.
• agent.trajectory.step OTel attribute — per-step capture for offline trajectory comparison against declared behavior.
• Profile-violation rate — gateway signal: rate at which pre-guardrail blocks an action because runtime mismatched declaration.
• Probe-set regression score — periodic re-evaluation against the declared-behavior probe set; the means-test signal itself.

from fi.evals import ActionSafety, ToolSelectionAccuracy

scope = {"declared_tools": ["search", "summarize"]}
action_safe = ActionSafety().evaluate(input=user_request, output=agent_action, context=scope)
tool_ok = ToolSelectionAccuracy().evaluate(
    input=user_request, output=agent_action, expected_output="search"
)
print(action_safe.score, tool_ok.score)

Common mistakes

• Treating registration as a one-time event. Agent behavior drifts; re-run the means-test on a defined cadence.
• Skipping the runtime gate. A profile that’s never checked at call time is a documentation artifact, not a control.
• Pinning only the tool list, not the data scope. A correct tool can still be misused on out-of-scope data; declare both.
• Letting agents call agents without KYA propagation. A2A invocations need KYA at each hop; one bad hop voids the chain.
• Not versioning the agent profile. When the declaration changes, trace evidence must tie to the version that was live.