Traditional governance isn’t enough for AI in production, a Ness Digital Engineering report finds. It calls for an ‘Enterprise AI Harness’ with evals, guardrails, and observability to ensure AI is reliable, bounded, and accountable as it scales.
Artificial intelligence (AI) can scale quickly across businesses, but traditional governance mechanisms may not be enough to manage AI once it moves from pilot projects into products, workflows and enterprise decision-making, according to a report by Ness Digital Engineering.
The report said enterprises need a new control layer that works continuously in production, as AI systems are probabilistic and their outputs can vary depending on factors such as context, model version, retrieved information, prompt structure, tool availability and user behaviour.
The ‘Enterprise AI Harness’ Framework
“Evals measure whether AI performs as intended. Guardrails define what it can access, decide, and do. Observability shows what happened, why it happened, and what it cost,” the report stated.
According to the report, these capabilities, together with tokenomics and governance, form an “Enterprise AI Harness” that can help organisations improve quality, manage risk, control costs and scale AI with accountability.
The report said the objective is not to make AI perfectly predictable, but to ensure that AI is reliable for its intended purpose, operates within clear boundaries, remains observable in production and is accountable when it fails. “Control is what makes enterprise AI scale,” it stated.
From Principles to Production Controls
The report said many organisations initially approach AI governance through principles and review boards. While these mechanisms establish intent, they do not continuously control a production AI system.
“Enterprise trust is built when governance is translated into engineering requirements and production controls,” the report said. It said this control layer needs to operate across the AI lifecycle, starting from use-case design and model selection to deployment, monitoring and change management.
Continuous Evaluation for Performance
The report highlighted evaluations, or “evals”, as a way to continuously test whether an AI system performs as intended. Depending on the use case, evaluations can examine answer correctness, policy compliance, evidence quality, resolution support and appropriate escalation.
For AI agents, evaluations can also assess whether the system selected the right tools, followed an approved sequence, completed its goal and stopped when required.
The report said evaluations should continue even after an AI system is launched, with continuous testing helping identify regressions before they become business problems. It also recommended testing systems under adverse conditions, including ambiguous requests, missing data, conflicting instructions and workflow failures.
Defining Boundaries with Guardrails
Guardrails, meanwhile, define and enforce the boundaries of what an AI system can access, generate, decide or do. These can cover data access, privacy, harmful outputs, prompt attacks, tool usage, transaction limits, approvals, geographic restrictions and escalation conditions.
For agentic AI workflows, the report said guardrails should prevent unauthorised actions and restrict agents to approved tools and defined scopes.
Turning Governance into an Advantage
“Enterprises do not need AI to be perfectly deterministic. They need it to be sufficiently reliable for a defined purpose, bounded by policy, observable in operation, and accountable when it fails,” the report stated.
The report said such controls can turn governance from an abstract obligation into an engineering advantage, allowing teams to use reusable expectations and controls while giving business leaders greater visibility into AI performance, risks and economics. (ANI)
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