Evaluating AI Agent Platforms: A Guide For Buyers

The arrival of AI agents is changing how organisations assess technology. As AI agents become central players in knowledge work, the familiar software playbook is quickly becoming irrelevant. 

Today’s buyers need to rethink criteria, moving past surface-level features to focus on risk and outcomes. This means looking beyond metrics such as task completion rates and speed to consider the broader impact.

As Magentic’s CTO, building safety and trustworthiness into our platform has always been mission-critical. While the rules are still being written, this is our checklist of non-negotiables every leader should consider when evaluating AI agent vendors.

Does the agent provide a reasoning trace?

Reasoning capabilities are fundamental. The planning component of an AI agent—the part that decides “what should I do next and why?”—is often the weakest link in enterprise deployments. 

At a minimum, agents should clearly outline the steps they took to complete a task and provide evidence supporting each action. For instance, if Magentic’s agents flag a missed volume discount, they pinpoint the exact contract clause and related invoices.

Look for:

• Reasoning trace: A clear ‘Chain of Thought’ that maps decision pathways step-by-step

• Source inspection: Easy access to the original data and documents underpinning the decision

• Transparent logs: Every step recorded for easy auditing, highlighting tools used, data retrieved, and the rationale behind each action

This level of insight is crucial for two reasons: reinforcing trust and catching errors before they multiply. At Magentic, we push this one step further with a “multi-agent review” system. It allows agents to verify their peers’ output, which limits errors and improves the quality of work.

Does the platform offer human-in-the-loop (HITL) controls?

HITL controls build intentional human oversight into AI workflows to validate automated decisions.

Take Magentic’s AI Agent Sam. He scans supplier contracts and flags anomalies—like unusual payment terms—but the final call to negotiate rests with the procurement leader, who weighs factors like supplier relationships, risk exposure, and long-term partnership value.

What good HITL looks like:

• Intentional human checkpoints at critical junctures, pausing AI workflows to confirm sensitive decisions

• Defined escalation paths that route uncertain or edge cases to human experts for review

• Clear governance frameworks outlining authority, responsibilities, and decision rights across AI-human interactions

This helps teams move quickly while remaining in control.

Are the AI agents model agnostic?

The AI agent platform should be foundational model (LLM) agnostic. This means that rather than being tied to a specific LLM, it can switch between multiple models depending on the task.

In procurement, this matters because a model tuned for automating purchase order processing isn’t always the right fit for, let’s say, drafting strategic supplier negotiation emails.

While proprietary models offer an extra layer of control, they also create vulnerabilities, exposing your organisation to risks when regulations evolve or superior models emerge. 

The AI landscape moves fast. What’s state-of-the-art today could be obsolete next year. That’s why flexibility matters. How easily can you update or swap models when requirements shift, or better options appear? Locking all your workflows into a single model risks falling behind as the market evolves.

Is the platform future-proof?

Adding to the challenge, AI regulations are evolving just as rapidly as the models themselves. To steer clear of costly missteps, choose platforms designed with auditability, traceability, and adaptability baked in from the start.

Key features to prioritise include:

• End-to-end audit trails: Detailed logs capturing every AI interaction, timestamped and contextualised

• Governance controls: Built-in safeguards like role-based access, human-in-the-loop reviews, and automated compliance checks that ensure AI actions are monitored and aligned with legal and ethical standards

• Explainability tools that make it easier for regulators and auditors to understand how AI behaves and why decisions are made

Together, these capabilities provide the accountability needed to meet growing regulatory demands around explainability and traceability in AI workflows.