Agentic AI is the most hyped segment in enterprise software. Vendors claim their systems can plan, execute, and adapt with minimal human oversight. For mid-market firms, the stakes are high: a wrong purchase can mean wasted budget, operational disruption, and compliance exposure. Yet the same firms cannot afford the enterprise-grade legal and technical teams that large corporates deploy for AI procurement.
This article outlines a practical audit framework for mid-market buyers. It separates what can be verified from what remains uncertain, and it flags the commercial terms that deserve scrutiny before signature.
Why the audit is necessary
Agentic AI differs from earlier automation tools. Traditional robotic process automation follows fixed rules. Agentic systems are supposed to make decisions within defined boundaries. That promise is attractive, but it also introduces new failure modes. A system that acts autonomously can make mistakes that are harder to trace and correct.
Mid-market firms are particularly exposed. They often lack the internal AI governance structures of larger organisations. They may also rely on a single vendor for a critical workflow, increasing the impact of any failure. As a result, procurement teams are moving from feature checklists to outcome-based validation.
The shift is visible in procurement patterns. Buyers are asking for evidence of autonomy in controlled environments, not just vendor slideware. They are also demanding clearer definitions of what the system can and cannot do. This is a response to a market where the term 'agentic' is applied loosely.
Step 1: Define the autonomy boundary
Before evaluating any vendor, define what autonomy means for your specific use case. Autonomy is not binary. A system that can draft emails is less autonomous than one that can negotiate contracts. The level of autonomy you need depends on the risk tolerance of your organisation.
Create a written specification that includes:
- The specific decisions the system is allowed to make without human approval.
- The decisions that require human sign-off.
- The escalation path when the system encounters ambiguity.
- The metrics that will define success, such as error rates or time saved.
This specification becomes the basis for vendor evaluation. It also helps you compare vendors on a like-for-like basis. Without it, you are comparing marketing claims rather than capabilities.
Step 2: Request evidence, not demos
Demos are designed to impress, not to inform. They show ideal conditions and curated examples. For a meaningful audit, request evidence that reflects your actual operating environment.
Ask for:
- Case studies that include failure rates and recovery times, not just success stories.
- Access to a sandbox environment where you can run your own test scenarios.
- Documentation of the system's decision-making logic, including how it handles edge cases.
- Logs from pilot deployments that show human intervention rates.
If a vendor cannot provide this evidence, treat that as a red flag. A genuine agentic system should be able to demonstrate its behaviour in a controlled setting. The absence of such evidence suggests either limited maturity or a desire to hide limitations.
Step 3: Test for true autonomy
A common vendor trick is to label a system as 'agentic' when it is actually a rules-based workflow with a conversational interface. To test for true autonomy, design scenarios that require the system to adapt to unexpected inputs.
For example, if the system is supposed to handle customer service tickets, introduce a ticket that falls outside its training data. Observe whether it:
- Asks for clarification.
- Escalates to a human.
- Attempts a solution that is plausible but wrong.
- Fails silently.
Document the outcomes. A system that escalates appropriately may be more valuable than one that attempts to solve everything autonomously. The goal is not to maximise autonomy but to find the right balance for your risk profile.
Step 4: Scrutinise the commercial terms
Agentic AI contracts often contain terms that shift risk to the buyer. Pay particular attention to:
- Liability caps: Many vendors cap liability at the value of the subscription fees. This may be inadequate if the system causes significant operational damage.
- Service level agreements (SLAs): Ensure SLAs cover not just uptime but also accuracy and response times for autonomous actions.
- Data usage rights: Confirm that your data is not used to train models for other customers without explicit consent.
- Exit provisions: Understand what happens to your data and workflows if you terminate the contract. Can you export your configurations and logs?
Mid-market firms should negotiate for liability caps that reflect the potential impact of a failure. If a vendor refuses to adjust the cap, consider whether the risk is acceptable.
Step 5: Plan for human oversight
Even the most advanced agentic systems require human oversight. The question is not whether to have humans in the loop, but where and how often. Define the oversight model before deployment.
Options include:
- Human-in-the-loop: A human approves every significant action.
- Human-on-the-loop: A human monitors the system and intervenes only when exceptions occur.
- Human-out-of-the-loop: The system operates autonomously, with post-hoc review.
For most mid-market firms, a human-on-the-loop model is a sensible starting point. It balances efficiency with control. As the system proves itself, you can adjust the level of oversight.
Step 6: Build a monitoring and review process
Once the system is live, you need a process to monitor its performance and review its decisions. This is not a one-time audit but an ongoing practice.
Set up:
- Regular reviews of system logs to identify patterns of errors or unexpected behaviour.
- A feedback loop where employees can report issues or suggest improvements.
- A quarterly review of the autonomy boundary to ensure it still matches your risk tolerance.
- A clear process for rolling back changes if the system degrades.
This monitoring is essential for maintaining trust in the system and for demonstrating compliance if regulators ask questions.
Commercial impact
The commercial impact of a rigorous audit is twofold. First, it reduces the risk of a failed implementation, which can cost far more than the software licence. Second, it gives you leverage in negotiations. Vendors who know you have done your homework are more likely to offer favourable terms.
A well-executed audit can also shorten the sales cycle. By defining your requirements clearly, you can eliminate unsuitable vendors early and focus on those that can meet your needs. This saves time for both your team and the vendor.
Risks and unknowns
The main risk is that the audit itself becomes a box-ticking exercise. If you do not have the technical expertise to interpret the evidence, you may still be misled. Consider bringing in an external consultant for the most critical evaluations.
Another unknown is the pace of regulatory change. Governments are still developing frameworks for AI accountability. A system that is compliant today may face new requirements tomorrow. Build flexibility into your contract to accommodate regulatory changes.
Finally, the technology itself is evolving. A vendor that is genuinely agentic today may be surpassed by competitors in a year. Do not lock yourself into a long-term contract without a clear upgrade path.
FY Outlook
The trend towards agentic AI is real, but the market is still immature. Mid-market firms that adopt a disciplined audit approach will be better positioned to capture the benefits while managing the risks. Expect to see more standardised evaluation frameworks emerge as the market matures.
In the near term, the balance of power in negotiations will shift towards buyers who can demonstrate a clear understanding of their needs. Vendors will respond by offering more transparent evidence and more flexible commercial terms. The firms that succeed will be those that treat agentic AI as a strategic investment, not a quick fix.
Conclusion
Agentic AI offers significant potential for mid-market firms, but only if the claims are validated. A structured audit that defines autonomy boundaries, tests real-world behaviour, and scrutinises commercial terms is essential. It is not about being sceptical of every vendor; it is about being confident that what you buy does what it promises.
The cost of a thorough audit is small compared with the cost of a failed deployment. By investing time upfront, you can avoid the expensive mistakes that come from trusting marketing over evidence.
Why It Matters
Mid-market firms are the primary target for agentic AI vendors, yet they often lack the procurement resources of larger enterprises. A failed deployment can cost significantly more than the software licence, making a structured audit essential for protecting budget and operations.
