Enterprise AI agents are no longer a proof-of-concept line item. By 2026, they sit inside production workflows at banks, insurers, healthcare networks, and global manufacturers - and the procurement process that precedes every deployment has grown proportionally rigorous.
This post is written for the people who actually sign off on these platforms: CIOs validating architecture fit, CISOs stress-testing security posture, legal and compliance teams mapping regulatory exposure, and procurement leads benchmarking vendors against a defined rubric. It is not a market overview. It is a structured checklist of what separates platforms that clear enterprise due diligence from those that stall in vendor assessment.
Why Procurement Has Become the Critical Path
Three converging pressures have made the evaluation cycle longer and more demanding in 2026:
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Regulatory crystallisation. The EU AI Act's high-risk provisions are now fully in force. DORA imposes operational resilience requirements on financial entities' ICT third-party dependencies - which includes AI vendors. ISO/IEC 42001:2023 has become the de facto AI management system standard that procurement teams request evidence of.
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Security incident history. Several high-profile prompt injection and agent hijacking incidents in 2024–2025 have made CISOs acutely aware that AI agents represent a new attack surface class, distinct from traditional SaaS.
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Budget scrutiny. After years of exploratory AI spend, CFOs now demand clear ROI gating, predictable token costs, and contractual SLAs before multi-year commitments.
Understanding these pressures is prerequisite to understanding why each criterion below exists.
Section 1: Architecture Suitability
1.1 Tiered Deployment Model
What to ask: Does the platform support a tiered architecture - engagement layer, agentic orchestration layer, and data/systems-of-record layer - with clean separation of concerns?
What to verify: Ask for a reference architecture diagram. The engagement layer (conversational interface, API gateway) should be cleanly separated from the orchestration layer (agent logic, tool dispatch, state machine). Data systems should never be accessed directly from the engagement layer without going through orchestration.
Red flag: Any platform where "everything runs through our chat interface" also means everything goes through their infrastructure with no customer-controlled isolation boundary.
1.2 Agentic Orchestration Engine
What to ask: What is the execution model for multi-step agent tasks? Is it a sequential pipeline, a parallel fan-out, a DAG, or a reactive event-driven model?
What to verify: Request a trace or execution log from a non-trivial workflow (5+ steps, at least one conditional branch) and inspect whether steps are executed as declared. Platforms that conflate "LLM API calls" with "agent orchestration" will show a flat sequence - real orchestration has state, branching, retry logic, and observable intermediate steps.
1.3 Data Layer Isolation
What to ask: Can the platform connect to customer-controlled data stores (customer-managed databases, private cloud storage, on-premise systems) without data transiting the vendor's infrastructure?
What to verify: Request a data flow diagram for a representative integration. Confirm that credentials for backend systems are stored in customer-managed secret stores (Vault, AWS Secrets Manager, Azure Key Vault) rather than the platform's own database.
Section 2: Identity and Access Control
2.1 Identity Federation
What to ask: Does the platform support SAML 2.0 and/or OIDC for enterprise identity federation? Can you bring your own IdP (Okta, Azure AD, Google Workspace, Ping)?
What to verify: Complete a test SSO flow from your IdP to the platform using a non-admin test account. Confirm that group memberships from the IdP map correctly to platform roles.
2.2 SPIFFE Workload Identities
What to ask: Does the platform support SPIFFE/SPIRE for workload-to-workload authentication? Can AI agents authenticate to downstream APIs using workload identities rather than static API keys?
What to verify: If the platform claims SPIFFE support, request documentation on the trust bundle distribution model. Confirm whether customers receive their own trust domain or operate within the vendor's trust domain.
2.3 RBAC and Attribute-Based Access
What to ask: Does the platform support fine-grained RBAC? Can you define roles that restrict which tools an agent can invoke, which data sources it can access, and which actions it can execute?
What to verify: Create a test role with deliberately limited permissions. Attempt to perform restricted actions via the API and UI as a user assigned to that role. Verify that both the UI and API enforce the same restrictions.
2.4 Audit Logging
What to ask: Are all administrative actions, agent executions, tool invocations, and data access events logged? What is the log retention period? Can logs be exported to a customer-controlled SIEM?
What to verify: Request a sample audit log export for a 24-hour period covering administrative changes, API calls, and agent executions. Confirm log integrity mechanisms (tamper-evident logging, cryptographic signatures).
Section 3: Regulatory Compliance
3.1 EU AI Act Compliance
What to ask: Has the platform been assessed against the EU AI Act? What risk classification does it claim - minimal, limited, high, or unacceptable? Has it been through a conformity assessment?
What to verify: Request the vendor's EU AI Act self-assessment or third-party conformity assessment report. Confirm the assessment covers the specific high-risk use cases you intend to deploy. Be aware that a vendor's "general compliance" claim is not the same as a use-case-specific conformity assessment.
For high-risk AI systems under Annex III, the vendor must provide: technical documentation (Article 11), a quality management system (Article 17), logging capabilities (Article 12), transparency obligations (Article 13), human oversight measures (Article 14), accuracy and robustness requirements (Article 15), and registration in the EU database (Article 51).
3.2 DORA Resilience
What to ask: Is the platform listed in your financial entity's ICT third-party risk register? Does the vendor provide a DORA-compliant contractual arrangement, including SLAs, audit rights, and exit provisions?
What to verify: Request the vendor's DORA-specific standard contractual clauses. Confirm they include: description of the service, data processing terms, SLA commitments with metrics, incident notification obligations, exit and transition support provisions, and audit rights.
3.3 ISO/IEC 42001 Certification
What to ask: Does the vendor hold ISO/IEC 42001:2023 certification for their AI management system? When was the last audit? What was the scope of certification?
What to verify: Request a copy of the ISO 42001 certificate and the most recent audit report (with redactions as appropriate). Confirm the certification scope covers the specific services you intend to use.
3.4 SOC 2 Type II
What to ask: Does the platform hold SOC 2 Type II certification? What are the Trust Service Criteria covered? When was the most recent audit period?
What to verify: Request the SOC 2 report (or executive summary under NDA) and review the auditor's opinions. Pay particular attention to any qualified opinions or exceptions noted.
Section 4: Security Posture
4.1 Zero Data Retention
What to ask: Does the vendor retain customer prompts, agent outputs, or conversation logs for model training or any other purpose? What is their contractual commitment on data retention?
What to verify: Review the Data Processing Agreement (DPA). Confirm that "zero data retention" is explicitly defined, not merely "data is not used for training." Request a technical description of how this is enforced - ideally through contractual commitments from the underlying LLM providers as well.
4.2 Prompt Injection Defenses
What to ask: What measures does the platform have against prompt injection attacks - both direct (user-injected) and indirect (data source poisoning)?
What to verify: Request documentation on the platform's prompt injection mitigations. Conduct a red-team test against a sandboxed instance using known prompt injection techniques. Assess whether the platform has input sanitisation, context length limits, or adversarial training measures.
4.3 Agent Hijacking Controls
What to ask: Can one agent session influence or hijack another agent's execution context? Are there isolation boundaries between concurrent agent runs?
What to verify: Attempt a cross-session attack in a test environment - inject unexpected tool calls or state modifications between concurrent agent executions. Verify that session state is isolated.
4.4 Network Segmentation and Encryption
What to ask: Is all traffic between agents, tools, and data sources encrypted in transit (TLS 1.3 minimum)? Is there network segmentation between tenant environments?
What to verify: Inspect TLS certificates on all platform endpoints. If multi-tenancy is used, confirm that network policies prevent cross-tenant data access at the infrastructure level.
Section 5: Operational Resilience
5.1 SLA Commitments
What to ask: What SLAs does the vendor offer for the core orchestration platform? Do SLAs cover availability, latency, and incident resolution? What are the remediation terms for SLA breaches?
What to verify: Review the SLA document carefully. Calculate the financial impact of downtime under the proposed SLA - is the remedy proportionate to your exposure? Be wary of SLAs that cover "platform availability" but exclude agent execution latency.
5.2 Incident Response
What to ask: What is the vendor's incident response process? Do they provide a dedicated security contact for critical vulnerabilities? What are the notification timelines for security incidents?
What to verify: Request the vendor's security incident response plan and SLA for security vulnerability disclosure. Confirm whether they participate in coordinated vulnerability disclosure (CVD).
5.3 Disaster Recovery and Business Continuity
What to ask: What is the RTO (Recovery Time Objective) and RPO (Recovery Point Objective) for the platform? Where are the backup data centres? Can you run a disaster recovery test?
What to verify: Review the DR documentation. If your use case is business-critical, negotiate a DR test clause into the contract.
Section 6: Cost and Commercial Terms
6.1 Token Cost Transparency
What to ask: How are token costs calculated and attributed? Are there separate charges for input tokens, output tokens, and system prompts? What visibility do you have into per-agent, per-workflow, and per-user token consumption?
What to verify: Request a cost breakdown from a representative production workload - not a demo scenario. Compare against your own estimate of token consumption for the same workload.
6.2 Model Routing Cost Control
What to ask: Does the platform support intelligent model routing that automatically selects the most cost-effective model for each task? Can you set cost budgets per workflow?
What to verify: Run identical tasks through the platform and a direct API call to the most expensive model. Compare costs and quality. Assess whether the routing decisions are observable and controllable.
6.3 Exit and Data Portability
What to ask: What happens to your data and workflows if you decide to leave the platform? Can you export all agent configurations, execution logs, and integrations in an open format? What transition support does the vendor provide?
What to verify: Review the exit provisions in the contract. Request a sample data export and verify that it contains everything you would need to re-implement your workflows on a different platform.
Section 7: Vendor Relationship
7.1 Roadmap Transparency
What to ask: How does the vendor communicate product roadmap changes? Do enterprise customers have input into roadmap prioritisation? What is the deprecation policy for features?
What to verify: Request a copy of the last two quarterly product update communications. Assess whether the vendor has a track record of adequate notice before feature deprecations.
7.2 Support Tiers and Response Times
What to ask: What support tiers are available? What are the response time SLAs for P1 (critical) incidents? Is dedicated support available?
What to verify: Review the support SLA document. Contact the support team with a technical question under the proposed support tier and measure response time.
7.3 Customer References
What to ask: Can you speak with customers in your industry who have run production workloads on the platform for at least 12 months?
What to verify: Speak with at least two reference customers. Ask specifically about: production reliability over 12+ months, how the vendor handled any incidents, whether token costs matched projections, and whether the platform has kept pace with their evolving requirements.
Putting It Together
No platform will score perfectly on every criterion. The checklist above is designed to help you weight each section according to your organisation's specific risk profile, regulatory obligations, and operational priorities.
A financial services firm operating under DORA should weight Sections 3 and 5 heavily. A healthcare organisation navigating GDPR and FDA software validation requirements should focus on Sections 3 and 4. A manufacturing firm deploying agents on the plant floor should prioritise Section 1 and 6.
The platforms that survive this checklist - and more importantly, the vendors that respond to it with evidence rather than marketing decks - are the ones worth taking through to a proof-of-concept with your actual workloads and your own data.
At Mindra, we built our enterprise evaluation kit specifically to help procurement and security teams work through exactly this checklist. If you are currently running a vendor assessment, request our security and compliance documentation package - it includes our ISO 42001 certificate, SOC 2 Type II report, EU AI Act conformity assessment summary, DORA standard contractual clauses, and full technical reference architecture.
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Written by
Mindra AI
Author at Mindra
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