No Verifier, No Agent: Agentic AI Pays Where Correctness Is Cheap to Check

Most agentic AI portfolios are assembled the wrong way round. Someone lists the processes that consume the most headcount, ranks them by cost, and points agents at the top of the list. Six months later the pilot is technically impressive and commercially stranded, because nobody can say whether the agent's output was right without a human redoing the work. The useful selection criterion is not how expensive is this process. It is how cheaply can we verify the result. Where verification is cheap, deterministic, and already instrumented, autonomy compounds. Where it isn't, you have bought a very expensive suggestion engine.

The cancellation wave is a verification problem wearing a governance costume

Gartner's widely quoted June 2025 forecast — that more than 40% of agentic AI projects will be canceled by the end of 2027 — attributes the failures to escalating costs, unclear business value, and weak risk controls. MIT's 2025 "GenAI Divide" study, which reported that the overwhelming majority of enterprise GenAI pilots delivered no measurable P&L return, points in the same direction. Read both carefully and the shared root cause is narrower than "governance": these programs never had a cheap, automatic way to decide whether a given agent run was good.

Without that, three things happen in sequence. Humans review everything, so the labour saving evaporates. Reviewers get bored and rubber-stamp, so quality risk appears. Finance asks for evidence of benefit and receives anecdotes. The project dies — not because the model was weak, but because the workflow had no oracle.

What a real verifier looks like

A verifier is any mechanism that scores an agent's output faster and cheaper than producing it. Software engineering is the clearest example — the test suite and the build are the verifier, which is why coding agents crossed into production first. The same structure exists in plenty of enterprise processes, and it is usually already built:

• Reconciliation checks: three-way match in procure-to-pay, bank-to-ledger reconciliation, intercompany netting. The agent proposes; arithmetic adjudicates.
• Schema and contract validation: master data enrichment, product content, and catalogue onboarding where a data contract or IDoc/API schema rejects malformed output automatically.
• Simulation and dry-run: pricing changes, ATP re-promising, or configuration edits that can be executed against a sandbox before commitment.
• Downstream acceptance signals: did the customer re-open the ticket, did the payment clear, did the order ship without a manual touch? Slow, but genuinely objective.
• Dual-source agreement: two independent extraction paths over the same document that must agree before posting, with disagreement routed to a human.

If your candidate workflow has none of these, the honest first project is not an agent. It is building the verifier — which is ordinary engineering work in integration, data quality, and observability, and which pays for itself even if you never deploy an agent at all.

Verification economics decide the autonomy level

Once you measure verification cost, the design decisions stop being philosophical. Cheap, instant, deterministic verification justifies closed-loop autonomy with sampled human audit. Cheap but delayed verification — you find out in three days whether the decision held — justifies autonomy with a compensating action path and tight blast-radius limits. Expensive or subjective verification means the agent drafts and a human disposes, and you should budget it as a productivity tool, not an FTE replacement.

McKinsey's QuantumBlack teams have made a similar point in their work on enterprise-ready agents: rigorous evaluation infrastructure sits alongside architecture as a first-class capability, not a post-launch add-on. In practice that means the evaluation harness ships before the agent, runs in production continuously, and is owned by the same team that owns the process.

Where this lands in an SAP-heavy landscape

Enterprises running SAP have an advantage they rarely exploit: the ERP is already a verifier. Postings either balance or they don't. Documents either clear or sit in a workflow queue. The constraint is usually access and latency — agents can see the transaction but not the audit trail, or they act faster than the reconciliation cycle can confirm. Closing that gap is mostly exposing existing controls as callable, low-latency checks. That is well-trodden SAP integration and extension work, not frontier AI research, and it is the difference between a demo and a deployed workflow.

The portfolio question to ask next quarter

Re-rank your agentic backlog by verification cost, not process cost, and the list will change substantially. Some glamorous use cases fall to the bottom. Some unglamorous ones — invoice exceptions, catalogue hygiene, dunning, returns disposition — rise, because the checking is already automated and the volume is real. As models keep improving, the binding constraint will shift further away from generation quality and toward the enterprise's ability to grade its own work at scale. The organisations that invest in verifiers now will be able to raise autonomy as a dial setting rather than a leap of faith.