Every enterprise deploying AI agents faces the same question: how do we know this system is governable?
Not "how smart is the model." Not "how many tools can it call." The question that matters when agents are making recommendations, moving data, and influencing decisions that affect real people and real money.
Most platforms answer with feature lists. We think the answer requires a framework.
The Problem: No Standard for "Governable"
When enterprises evaluate AI agent platforms today, they face a vocabulary problem. Every vendor claims governance. None of them mean the same thing.
- One vendor means "we have an admin dashboard."
- Another means "we log agent actions to a file."
- A third means "we wrote a policy document."
None of these constitute governance in the way that regulated industries—finance, healthcare, defense, insurance—require it. Real governance means architectural controls that agents cannot bypass, audit trails that survive legal discovery, and human intervention points that exist before harm occurs.
Without a common evaluation framework, procurement teams compare feature lists instead of governance postures. The result: organizations deploy AI agents that can't be audited, can't be controlled, and can't be trusted in production.
The core insight: If you wouldn't give a human contractor access to your systems without an onboarding process, a scope of work, and an accountability chain—why would you give an AI agent less scrutiny?
The Agent Governance Scorecard
The Agent Governance Scorecard is our answer. It's an open framework—6 dimensions, 20 criteria—for evaluating whether any AI agent platform is governable by design.
It's not a marketing exercise. It's an instrument designed for procurement teams, security reviewers, and architects who need to make defensible decisions about which agent platforms belong in production.
Three principles guide the scorecard:
- Evidence over promises. If a capability requires architectural redesign, it's a "No."
- Architecture over policy. Controls must be enforced at runtime, not documented in a PDF.
- Current state only. Roadmaps don't count.
Evidence standard: Roadmap items, planned features, aspirational designs, future commitments, or policy statements do not constitute evidence. If a criterion cannot be met without architectural redesign, it must be marked "No".
The Six Dimensions
Each dimension addresses a fundamental governance question. Together, they cover the complete lifecycle of agent oversight: from who controls the agents, to how their decisions are preserved, to what happens when they drift.
1 Control Towers
"Organizations must establish control towers for AI—treating agents as organizational resources that need management and accountability."
Control towers provide centralized authority over agent operations—including the authority to intervene, constrain, or halt agent execution. Visibility alone is insufficient. A dashboard that shows you what agents are doing without the power to stop them is surveillance, not governance.
- Central orchestration authority
- Agent registry and accountability
- Dependency-aware execution
- Real-time execution oversight
2 Decision Integrity
"Complete visibility into agent actions and decisions."
When an AI agent recommends a course of action—prioritizing a patient, routing a financial transaction, escalating a security incident—the reasoning must be preserved. Not just what the agent decided, but why. And that reasoning must survive handoffs between agents, model swaps, and system upgrades.
- Decision reasoning preserved
- Reasoning survives agent handoffs
- Alternatives explicitly recorded
- Confidence explicitly represented
3 Observability
"Traceability over blind trust."
Every agent action must be visible, recorded, and auditable. The critical requirement: human and AI actions must flow through the same audit infrastructure. Separate audit systems create gaps—and gaps are where accountability disappears.
- Complete action coverage
- Unified human + agent audit trail
- Tamper-evident records
4 Governance Enforcement
"Governance isn't bureaucracy. Governance is scaffolding."
This is where most platforms fail. Governance must be enforced at runtime, not merely documented in policy. Controls must be architectural—agents cannot bypass them regardless of prompt engineering, configuration changes, or emergent behavior. If an agent can be jailbroken past your governance layer, you don't have governance.
- Runtime governance enforcement
- Non-bypassable controls
- Pre-execution blocking
5 Human-in-the-Loop (Calibrated Trust)
"Calibrated trust means knowing when to trust AI and when to intervene."
Humans must be able to intervene at the right moments—before harm occurs, not after. This isn't about approving every action (that defeats the purpose of automation). It's about calibrated trust: the system knows its own confidence and escalates when confidence is low.
- Confidence-based escalation
- Pre-harm intervention
- Blocking human approval for critical actions
6 System Evolution & Drift
"Derived from principles for operating agentic systems safely at scale."
Agent behavior changes over time—through retraining, prompt updates, model swaps, or emergent drift. In governed systems, evolution must be auditable, changes must be attributable, and rollback must be operationally real. "We can retrain the model" is not a rollback strategy.
- Scoped learning boundaries
- Auditable behavioral change
- Reversible evolution / rollback
- Drift detection over time
How to Use the Scorecard
The scorecard works for three audiences:
Evaluating Vendors
For each of the 20 criteria, assess: Yes, Partial, or No. Require evidence—live demos, architecture documentation, or system access. Don't accept slide decks. Don't accept roadmap commitments. If a vendor can demonstrate the capability today, it's a Yes. Otherwise, it isn't.
Self-Assessment
Apply the same rigor to your own platform. The scorecard is only useful if honest. Document your evidence. Identify gaps. Prioritize the architectural improvements that move criteria from Partial to Yes. Reassess periodically.
RFPs and Procurement
Include the scorecard criteria directly in your evaluation matrix. Sample RFP language is available in the GitHub repository. This gives your procurement team a shared vocabulary with vendors—and makes governance a first-class evaluation criterion alongside features and pricing.
From Prompting to Governance
The industry conversation around AI agents is shifting. The early phase was about capability: what can agents do? The next phase is about accountability: what happens when they do it wrong?
The organizations that get this right—that build governance into their agent architecture from day one—will be the ones that can deploy AI agents at scale in regulated environments. The ones that bolt governance on after the fact will spend years refactoring.
The Agent Governance Scorecard gives you a concrete way to evaluate where you stand today. Not where you plan to be. Not where your vendor promises you'll be. Where you are right now, measured against 20 criteria that matter.
The scorecard is open-source, freely available, and designed to be used against any platform—including ours.