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This post was created in collaboration with micro1.
AI agent adoption is moving faster than most security and compliance teams can keep up with. The landscape looks promising, but often opaque:
An AI agent approves a high-value transaction. On whose behalf?
Another modifies records in a production database. Did it have permission to do that?
A third surfaces clinical recommendations that a physician uses to make a care decision. Is it trustworthy?
Most engineering teams struggle to answer these questions with the specificity that regulations and best practices require. The regulatory pressure mounting on AI interactions has become a major sticking point, stalling production deployments and even shutting down would-be pilots.
The EU AI Act, for example, becomes fully applicable in August 2026. It mandates human oversight, risk management systems, and post-market monitoring for high-risk AI interactions. Similarly, the OWASP Top 10 for Agentic Applications formalizes the security risks specific to autonomous AI. Every one of its ten threat categories cite authentication and authorization mitigations.
These frameworks join existing requirements like HIPAA, PCI DSS, and GDPR to form a consistent template for AI regulatory compliance. If an AI system acts on sensitive data, every action needs attribution, every access decision needs clear justification, and the whole sequence needs to be auditable after the fact.
The question we face now is not whether organizations will need to observe and prove this chain of events, but how. In the following article, we discuss the infrastructure needed to achieve visibility into agentic flows, from their initial inception to production deployment.
Understanding the chain, from expert auditing to agent interaction
An AI agent touching sensitive data in production represents the final step of a much longer process: training, auditing, re-training, standing up security infrastructure, scoping, and so on. Before the agent can be expected to act reliably, someone with domain expertise had to validate the data it was trained on, evaluate its outputs under realistic conditions, and confirm that its results met the standards beyond reproach.
After deployment, the agent connects to tools, accesses systems, and takes actions on behalf of users, often across organizational boundaries. These two dimensions break into distinct but deeply interrelated problems that organizations tend to address separately. But they should be solved together.
Was the agent trained and evaluated by qualified humans?
Generic benchmarks do not capture how an AI agent actually performs inside a specific, specialized workflow. An agent might pass standardized tests while consistently failing on edge cases, compliance requirements, and procedural nuances that generally define the real-world applications it’s expected to tackle.
This is where structured human evaluation becomes a foundational component throughout the agent’s development and post-production lifecycle. micro1’s agentic AI evaluation is built around context, grounded in real tasks, decisions, and success criteria rather than synthetic benchmarks. Domain experts thoughtfully evaluate agent outputs in realistic scenarios to surface gaps in reasoning, edge case failures, and risky behavior that automated testing simply cannot.
Evaluation doesn’t stop at measurement. It powers a continuous improvement loop. Results from expert assessments drive targeted data generation and fine-tuning, so agent performance not only improves but stays reliable over time. A dedicated AI agent performance management system like this can offer quantifiable metrics at a more granular level, offering deterministic monitoring for the non-deterministic entities it observes.
This is especially critical for regulatory compliance. A well documented, repeatable evaluation is exactly the kind of evidence an auditor will ask for when reviewing whether or not an AI system meets required standards.
What can the agent access, on whose behalf, and can you prove it?
With qualified humans evaluating and guiding the agent’s behavior, the next step is enabling the agent itself to access sensitive data so it can operate effectively in production workflows. They need to connect with tools and cross-organizational systems, and all of these interactions need to be scoped, time-bound, consented, and auditable.
Traditional identity systems were not built for this: static API keys and service accounts simply do not suffice for when a task demands elevated access on-the-go or when a session should expire. This is why agentic identity treats AI agents as first-class identities alongside human users, with infrastructure purpose-built for the way agents actually operate.
An agentic identity provider like Descope ensures:
Agents receive ephemeral, scoped credentials that expire automatically, so no agent retains standing access to downstream services
Access is governed by granular policies that evaluate context from the agent, user, tenant MCP server, and target service before granting any permissions
Step-up authentication is in place for sensitive operations to offer an extra human-in-the-loop checkpoint; the system doesn’t just log that an action took place, but that the right person authorized it in real time
The audit trails this infrastructure produces are detailed and exportable, forming a comprehensive and cohesive picture alongside the expert evaluations discussed above.
Two agentic dimensions, one unified audit
A Descope survey of over 400 identity decision-makers found that while 88% were using or planning to use AI agents, only 37% had progressed past pilots. In many cases, the reason these projects stall on the runway is not due to technological limitations, but tactical ones.
Leaving output quality and governance, both critical to auditability, until late in the game can lead to painful backpedaling and lost cycles. The regulatory requirement stoking this urgency is a simple, singular demand: prove your AI systems were built, evaluated, and deployed according to standards and best practices. Then show your work.
That proof requires two distinct pieces of evidence:
An auditable chain showing qualified human oversight across the agent’s training and post-training evaluation, during its inception and prototyping (and beyond)
Observable and logged access governance across the agent’s operational lifetime, into its later development and production deployment
These two elements of visibility neatly dovetail into a unified audit that says, “We did our due diligence, deployed our agent responsibility, and will continuously monitor its behavior in accordance with regulations and/or best practices.”
This is the record that shows what the agent can access, what it actually accessed, on whose authority, with what consent, and whether any access was blocked or permissions were granted progressively. It’s an audit trail that denotes expert data quality, velocity, and reliability at the individual level, producing the kind of structured and traceable documentation that compliance teams need.
Organizations that treat these as afterthoughts tend to discover that the integration is harder than it would’ve been at the start. The expert-driven evaluation and improvement layer surfaces where agents fail, why those failures occur, and what needs to change to resolve them, creating a clear path to stronger performance and, at times, compliance. In parallel, the identity layer enforces governance and provides auditability, capturing what the agent did, where, on whose behalf, and within what permission scope.
These audits can even feed back into the evaluation process, informing your continued refinement of outputs: should the agent have permission to view this, or should it stick to less sensitive data sources? And while building these two elements in parallel is often efficient, it’s also a clean and mappable way to show the chain of custody that regulators, compliance teams, and enterprise customers are beginning to demand.
Agents don’t fail today because they lack capability. They fail because they lack context, oversight, and control. Evaluation and identity are what turn potential into reliability, and reliability is what turns agentic AI into real business impact.
To learn more about agentic identity and how to get from prototype to production, subscribe to the Descope blog, follow our socials (LinkedIn, X, and Bluesky), and join our dev community, AuthTown.
