Production deployment of AI agents presents a fundamental tension for technology leaders. Organizations recognize the potential for increased productivity and reduced operational costs, yet they grapple with unfamiliar risks arising from AI capabilities that operate differently from conventional software. When an agent independently makes decisions and executes actions, distinguishing between optimal performance and catastrophic failure becomes difficult to predict in advance.

The core challenge lies in maintaining organizational oversight as agent capabilities expand. Striking the right balance between enabling innovation and managing risk depends on the ability to evaluate, quantify, and sustain confidence in agent behavior. The principle is straightforward: agents should gain greater autonomy only as they demonstrate trustworthiness.

Trust as a line of credit

Credit card risk management offers a useful parallel. Financial institutions assess creditworthiness before establishing limits, then enforce real-time authorization and fraud detection after issuance. Unusual activity triggers heightened review. Consistent on-time payments improve credit scores but do not automatically raise limits.

AI agents warrant similar treatment. Before deployment, organizations should test agents for resilience, reliability, and performance characteristics. Based on these results, assign a trust score reflecting demonstrated capability. Following deployment, maintain continuous oversight. Subject significant actions to real-time policy checks and watch for anomalies. Treat evidence of sustained strong performance as validation for continued operation within existing risk parameters, not as justification for expanded autonomy.

Earning trust requires a structured DevOps-style cycle:

  • Conduct thorough testing before launch
  • Deploy with constraints derived from the trust score
  • Monitor agent activity in production
  • Identify failure modes, retest to confirm trustworthiness, and redeploy

This cycle continues indefinitely. Agent behavior can shift over time. Sustained testing and observation are essential to identify degradation in trustworthiness and implement corrective measures.

Agents gone feral

An agent that performs correctly during testing may appear stable after launch, yet this appearance can mask underlying instability. Multiple failure modes can cause agents to behave unpredictably after deployment:

  • Drift: Environmental changes render previous rules obsolete, causing unexpected agent responses
  • Malicious actors: Jailbreaks and prompt injections force agents to circumvent policies or misuse authorized tools
  • Ungoverned agents: Legitimate agents operating without sufficient monitoring, accountability, or formal evaluation processes
  • Multi-agent interactions: Individually authorized actions combine to produce outcomes that violate organizational policy, even absent intentional rule violations

Even rigorously tested systems can malfunction in production. Modifications to underlying models, prompts, available tools, or workflows can alter behavior after pre-deployment validation. Without ongoing observation and testing, agents can gradually develop problematic patterns.

Once you've trained your agent and it's doing the right things, it might appear domesticated. But appearances can be deceiving.

The solution involves establishing baseline behavior and implementing governance structures for each agent and its capabilities. Establish agent identity, validate performance, and continuously assess trustworthiness. The objective is an agent that acts according to the organization's interests, remains accountable to the organization, and resists degradation into feral behavior.

While guardrails play a role, they should not serve as the primary containment mechanism. Rather than relying on boundary collisions to correct course, agents should remain on designated paths, much like trains following tracks. These tracks represent policies defining the agent's purpose and organizational requirements. Governance frameworks extend this rail analogy across the entire agent lifecycle, converting policies into evaluation standards and operational controls.

Fiduciary: the destination

Defining trust is straightforward. Implementing it within an agent is substantially harder. The financial sector provides a useful standard: the fiduciary. A fiduciary maintains duties of care and loyalty within the defined relationship scope.

Trust is easy to define. But it's hard to program into an agent.

Under U.S. law, investment advisers operating as fiduciaries must prioritize client interests and cannot subordinate client welfare to their own.

The fiduciary concept serves as an analogy for desired agent behavior. For AI agents, fiduciary denotes an intended behavioral standard rather than legal fiduciary status or assured loyalty. The intent is serving organizational interests rather than model vendor interests. However, agents differ fundamentally from humans. They operate without conscious intent and, despite anthropomorphic language, lack personhood. For agents, fiduciary status represents a behavioral condition—an objective standard assessed through observable actions.

The objective is transitioning agents from feral to fiduciary as rapidly and efficiently as feasible. Organizations using Vijil have achieved a 4x reduction in time to trust.

Building trust into the agentic lifecycle

Vijil implements a continuous evaluation loop mirroring DevOps practices for code: build, ship, run, improve. Each phase validates agent reliability, security, and safety characteristics. Performance is measured and failure points are corrected. The system detects both improvements and declines in trustworthiness. Organizations requiring data residency can run on-premises, within their own cloud infrastructure, or in isolated environments. The hosted free tier transmits evaluation traffic and decision traces to Vijil.

Every deployed agent serves someone's interests. The critical question is whether that interest aligns with the organization's. Progression from feral to fiduciary begins with evidence, not assumption.