AI SRE Platform Buyer’s Guide 2026: A 12-Point RFP Framework

Every observability vendor now claims AI capabilities. Here is the 12-point evaluation framework that separates real AI SRE platforms from AI-washed dashboards, and the questions to put in your RFP.

Why Most AI SRE Evaluations Go Wrong

By 2026, every observability and incident-management vendor has added "AI" to their pitch deck. Some have built genuine agentic systems. Most have layered a chat interface on top of their existing alerting and dashboard products and called it AI. The two categories look identical in a 30-minute sales demo and behave radically differently in a real on-call rotation.

This guide gives you the 12 questions that separate a real AI SRE platform from a marketing relabel. Every question maps to a specific operational behavior you should be able to verify in a proof of concept. Send these questions in your RFP. Score the answers using the rubric at the end. The vendor that comes out on top will not be the one with the slickest demo; it will be the one whose architecture actually matches the autonomy, trust, and audit requirements of running production at agent speed.

1. What Tasks Does the AI Actually Execute Autonomously?

Why it matters: "AI assistance" and "AI autonomy" are different categories. An AI that suggests next steps to a human is a productivity tool. An AI that executes runbooks against production without asking permission is a different product entirely.

What to ask: "Give me a complete list of actions your AI can execute autonomously, with no human approval. For each, what is the blast radius and what triggers the execution?"

Red flags: "Our AI suggests actions for the engineer to approve" (this is a chat interface, not autonomous). "The AI can do anything if you give it the right permissions" (vague authority is dangerous authority). "We have not yet enabled fully autonomous remediation in production" (the autonomy is hypothetical).

2. What Is the Trust Model and Revocation Path?

Why it matters: Real autonomous AI needs a way to scope and revoke its authority. Without this, you cannot safely escalate AI privileges over time, and you cannot safely contain a bad actor (or buggy agent) when one is detected.

What to ask: "How does an agent earn additional autonomy? How do I revoke autonomy from a specific agent or class of agents in under 30 seconds when it misbehaves?"

Red flags: "Permissions are configured at install time" (no dynamic trust scoring). "Revocation requires opening a support ticket" (production safety should not depend on vendor SLA). "We have not had to revoke an agent yet" (untested escape hatches usually do not work).

3. Which Clouds, OSes, and Runtimes Are First-Class?

Why it matters: "Supports AWS" can mean anything from a deep integration with EC2, EKS, and IAM to a basic CloudWatch metric scraper. Real production environments span multiple clouds, multiple OSes (Linux and Windows), and multiple runtimes (containers, serverless, VMs, bare metal).

What to ask: "Which cloud APIs do you call directly? Which Kubernetes distributions have CI test coverage? Do you support Windows Server and Linux equally? List the runtimes (Docker, containerd, Lambda, ECS, GKE Autopilot, OpenShift) where you have customer references."

Red flags: "We support all major clouds" (vague claim). "Windows is on the roadmap" (will not work for hybrid environments). "Most of our customers are on EKS" (read: not battle-tested elsewhere).

4. What Is the Audit Format and Retention?

Why it matters: An autonomous agent that takes production actions must produce an immutable, queryable audit trail of every decision. This is required for compliance, postmortems, and trust calibration. Without a real audit ledger, you cannot answer "why did the agent do that?" three weeks later.

What to ask: "Show me a sample audit record for a single agent decision. What fields are captured? Where is the data stored? What is the default retention? Can I export to my own SIEM?"

Red flags: "The audit is in our application logs" (logs are not an audit ledger). "Retention is 30 days" (too short for any real compliance need). "Export requires a custom integration" (vendor lock-in via audit data).

5. What Is the Policy Graph Model?

Why it matters: Real autonomous platforms enforce policy at execution time, not at training time. This means you can write rules like "this agent may restart pods in staging but never in production" or "remediation actions on the payment service require a human approval until trust score reaches 95%." Without a policy graph, you have no way to express organizational guardrails.

What to ask: "Show me a sample policy that restricts an agent's authority by service, environment, and trust score. Where is the policy enforced? What happens when policy and AI judgment conflict?"

Red flags: "Policies are managed through the UI" (no code-based policy = no version control). "We use the AI's own judgment to enforce policy" (a black box enforcing a black box is not safety).

6. Does the Platform Read or Write Production State?

Why it matters: A read-only AI cannot remediate. A read-write AI can break things. Most teams want progressive autonomy: read-only at first, write access in narrow contexts after trust is earned. The platform must support both modes cleanly.

What to ask: "Can I deploy your platform in read-only mode? When an agent earns write access, is it scoped per-resource or all-or-nothing? Show me the code that enforces the scope."

Red flags: "We are read-only by default" (will not deliver autonomous remediation). "Write access is all-or-nothing" (no progressive trust). "We use IAM permissions to scope access" (IAM is too coarse-grained for agent-level scoping).

7. What Is the Integration Surface?

Why it matters: An AI SRE platform that only sees one signal (just metrics, just logs, just events) cannot do real correlation. The breadth and quality of integrations determines whether the AI has enough data to make good decisions.

What to ask: "List your top 50 integrations. For each, what data flows in (metrics, logs, traces, events, configuration)? What actions can flow out (create incident, restart service, scale up, page human)?"

Red flags: "We integrate with everything" (vague claim). "Most integrations are read-only webhooks" (no bidirectional flow = limited remediation). "Integration setup takes 2-4 weeks per tool" (poor integration ergonomics will kill adoption).

8. What Is the Cold-Start Time on a New Service?

Why it matters: AI agents need historical data to perform well. A platform that needs 90 days of data before it produces useful insights is a 90-day deployment to first value, which kills adoption.

What to ask: "When I deploy your platform on a brand-new service with zero historical data, when does it produce its first useful action? What does that action look like?"

Red flags: "After about 30 days of baselining" (too long). "It depends on data volume" (deflection). "First useful action is anomaly detection" (anomaly detection without baseline is mostly noise).

9. How Does It Handle Novel Incidents?

Why it matters: Most platforms perform well on known incident patterns. The hard test is the novel incident: a failure mode the platform has never seen. A real AI SRE platform should escalate cleanly to a human with full context, not silently fail.

What to ask: "Walk me through what happens when your AI encounters an incident pattern it has never seen. How does it decide to escalate? What context does it package for the human?"

Red flags: "Our AI handles all incidents" (impossible claim). "Novel incidents fall back to traditional alerting" (degrades to a regular pager). "We use the LLM to handle the novel case" (LLMs without context produce hallucinations, not action).

10. What Is the Per-Engineer Pricing at Your Team Size?

Why it matters: AI SRE platforms with consumption-based pricing (per-event, per-action, per-incident) create perverse incentives: the more value the platform delivers, the more it costs. Per-engineer pricing aligns vendor incentives with customer value.

What to ask: "Quote me a price for [our team size] including all features. Is that price subject to overage charges? What happens if the AI generates 10x the action volume next month?"

Red flags: "Pricing is consumption-based" (unpredictable). "Action volume is uncapped at our base price" (probably has a renewal-time gotcha). "We charge per managed entity" (will explode in cloud-native environments).

11. What Is the Multi-Tenant Data Isolation Model?

Why it matters: If the vendor is using your data to train models that benefit other customers, you have leaked competitive intelligence. If the vendor is not, the AI may be weaker than a single-tenant alternative. Both options have trade-offs and you need to know which one you are buying.

What to ask: "Is our data used to train your models? If yes, how do you ensure we cannot recover other customers' data through model probing? If no, how do you keep our model performance competitive without cross-tenant learning?"

Red flags: "Data is used in aggregate, anonymized form" (anonymization is rarely sufficient for high-cardinality data). "We do not use customer data" (then how is the model improving?). Refusal to put the answer in writing.

12. What Is the Compliance Posture?

Why it matters: Production AI in regulated industries (finance, healthcare, government) requires specific compliance certifications. A vendor that cannot show current SOC 2 Type II, ISO 27001, GDPR DPA, and (for US federal) FedRAMP cannot be safely used in those environments.

What to ask: "Provide your current SOC 2 Type II report, ISO 27001 certificate, GDPR Data Processing Addendum, and FedRAMP authorization status. What is your incident notification SLA?"

Red flags: "Compliance is on the roadmap" (not deployable in regulated environments). "We are SOC 2 Type I" (snapshot-in-time only, not enough). "Compliance docs are available after contract signing" (red flag for opacity).

How to Score the Responses

Score each of the 12 questions on a 0-3 scale: 0 if the vendor cannot or will not answer. 1 if the answer is vague or aspirational. 2 if the answer is concrete and demonstrable. 3 if the answer is concrete, demonstrable, and exceeds your minimum bar.

A score of 30+ across all 12 questions indicates a real AI SRE platform. A score below 20 indicates a marketing relabel. The middle range (20-30) requires a deeper proof of concept to determine which way the platform actually leans.

Three of the 12 questions are veto criteria: a 0 on autonomy (#1), trust model (#2), or audit (#4) should disqualify the platform regardless of overall score. These are the architectural foundations that cannot be added later.

For a worked example of how a real agent-native platform answers all 12 questions, see Nova AI Ops or read the Agentic SRE pillar guide.