RFP Guide: Evaluating AI Agent Governance Solutions

Why Your Standard Security RFP Will Fail for AI Agent Governance

If you're running an RFP guide for evaluating AI agent governance solutions using the same template you use for WAFs, SIEMs, or IAM platforms, you're going to end up with the wrong vendor. AI agents are not traditional software — they take autonomous actions, chain tool calls together, hold persistent credentials, and can cause real-world side effects without a human in the loop. Standard security evaluation criteria don't capture that.

According to Gartner, by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024. That means the governance decisions you make now will shape how your organization runs AI agents at scale. Getting the vendor selection wrong isn't just a procurement headache — it's a production incident waiting to happen.

This guide gives you a structured framework: the right evaluation criteria, the questions to ask vendors, and a comparison table for the major players in the space. Whether you're a solo engineering team building an internal agent or a platform team governing hundreds of them, the criteria below apply.

RFP Guide: The Core Evaluation Criteria for AI Agent Governance

Before you write a single question in your RFP, align your team on what "governance" actually means in the context of AI agents. There are three distinct layers where governance can operate, and most vendors only cover one or two of them.

Layer 1: Network and Identity-Level Controls

This is where most enterprise security vendors play — controlling which services an agent can reach, what credentials it holds, and how those credentials rotate. Tools like Okta AI Agent Identity and Astrix Security operate primarily at this layer. They're strong on non-human identity (NHI) management and OAuth token lifecycle, but they don't control what an agent does with that access once it's granted.

For more context on NHI management in agent contexts, see our guide on non-human identity management for AI agents.

Layer 2: Prompt and LLM Request Interception

Some vendors, like Difinity AI, intercept LLM requests and apply policy at the prompt layer. This catches prompt injection and data leakage at the model boundary, but it doesn't govern the downstream tool calls an agent makes. An agent can receive a safe-looking response and still execute a destructive API call.

Layer 3: Operation-Level Action Governance

This is the hardest layer and the most operationally important one. It means governing specific actions — "send this email," "execute this database query," "purchase this item" — against owner-defined rules, with the ability to require human approval, rate-limit, or block based on context. Very few vendors operate here. This is the layer your RFP should prioritize.

Our article on what agent governance actually means breaks down these layers in more detail if your team needs a shared definition before drafting RFP criteria.

RFP Guide: Eight Evaluation Criteria with Vendor Questions

Use the following criteria as your RFP scoring rubric. Weight them based on your team's priorities — a CISO-driven process will weight criteria 1–3 most heavily; an engineering-led process will weight 4–6.

1. Governance Depth: Where Does Control Actually Happen?

Ask every vendor to show you, technically, at what layer their product governs agent behavior. Request a demo that shows what happens when an agent attempts a destructive action — does the platform block it, log it, or route it for approval?

Questions to ask:

• Can you define policies at the individual tool-call or API operation level, not just at the connection level?
• What happens when an agent hits a blocked action mid-task? Does it fail gracefully or throw an uncaught exception?
• Can you require human approval for specific action types while allowing others to run autonomously?

2. Enablement vs. Pure Governance

A governance-only platform forces you to solve two problems separately: giving agents access to capabilities, and then governing that access. Platforms that combine both — providing the tools agents need (web search, email, B2B data, financial market data) alongside the governance layer — reduce integration surface area and operational overhead significantly.

Questions to ask:

• Do you provide managed integrations (email, web, databases, financial APIs) or do we bring our own?
• How many services can an agent connect to out of the box?
• Is the credential management for those connections handled by the platform, or do we manage secrets?

3. Framework Compatibility

Your agent governance platform needs to work with whatever framework your team already uses — Claude Code, Cursor, OpenAI Agents SDK, LangChain, LlamaIndex, or custom Python. Vendor lock-in to a specific framework is a significant long-term liability as the ecosystem evolves rapidly.

Questions to ask:

• Which agent frameworks do you officially support?
• Do you expose an MCP server, REST API, and CLI — or only one interface?
• What does integration look like for a custom agent built without a major framework?

4. Audit Trail Quality

For compliance and debugging, you need a complete record of what every agent did, when, with what inputs, and what the outcome was. Logging at the network level isn't enough — you need action-level traces that tie back to specific agent sessions and user contexts.

See our dedicated guide on AI agent audit trails for compliance teams for the specific fields your logs need to capture.

Questions to ask:

• What is the schema of an audit log entry? Can you show us a real example?
• Are logs immutable and tamper-evident?
• Can logs be exported to our SIEM in real time, or only via periodic pull?

5. Developer Experience and Time to Value

If your engineering team needs three weeks and a vendor-side implementation consultant to get a test agent running, you have an adoption problem before you have a governance problem. The best platforms are operable via API key and an npm install.

Questions to ask:

• How long does it take to go from account creation to a governed agent making its first tool call?
• Is your documentation public? (If not, why not?)
• Do you have a self-serve tier with real pricing published, or is everything "contact sales"?

6. Pricing Transparency and Scaling Model

Enterprise-only pricing with no published tiers is a red flag for developer tools. It signals that the vendor isn't targeting the teams that actually build agents. Ask for clear per-action, per-agent, or per-seat pricing with predictable scaling costs.

Questions to ask:

• What is the cost to govern 10,000 agent actions per month? 1,000,000?
• Is there a free trial or self-serve tier to validate the platform before procurement?
• Are there egress or data retention fees that don't appear in the headline price?

7. Operational Posture: Managed vs. Self-Hosted

Self-hosted open-source tools like DashClaw and AgentControl.dev give you full control but require your team to operate, patch, and scale them. Managed SaaS platforms take that burden off your plate but introduce a dependency on the vendor's uptime and security posture. There's no universally correct answer — it depends on your compliance requirements and engineering capacity.

Questions to ask:

• What is your SOC 2 status? (Type I or Type II?)
• What is your SLA for the governance control plane? What happens to agent workloads if your service goes down?
• For self-hosted vendors: what does the operational runbook look like at 50x current load?

8. Vendor Roadmap Alignment with MCP and Emerging Standards

The Model Context Protocol (MCP) is becoming the de facto standard for agent-tool communication. Your governance platform needs a clear, credible position on MCP — not just "we support it" but how they govern MCP connections specifically, and whether their approach locks you into their MCP implementation or works with any MCP server.

Questions to ask:

• Do you operate your own MCP server, or do you proxy third-party MCP servers?
• Can we connect our own MCP tools and have them governed by your platform?
• What is your roadmap for A2A (agent-to-agent) protocol support?

Vendor Comparison Table: AI Agent Governance Platforms

The table below maps major vendors against the eight criteria above. Ratings reflect publicly available information as of mid-2025 and vendor-published documentation.

Vendor	Governance Layer	Enablement Included	Framework Agnostic	Self-Serve Pricing	MCP Support	Managed SaaS
Handler	Operation-level	Yes (200+ services)	Yes (any framework)	Yes ($30/mo Basic)	Yes (own MCP server)	Yes
Okta AI Agent Identity	Identity/network	No	Partial (Okta ecosystem)	No (enterprise sales)	Limited	Yes
Astrix Security	NHI / token lifecycle	No	Yes	No (enterprise sales)	Limited	Yes
Oasis Security	Identity / CISO-focus	No	Yes	No (enterprise sales)	No	Yes
Speakeasy	MCP governance	No	Partial (MCP only)	Limited	Yes (MCP-focused)	Yes
Difinity AI	LLM prompt/request	No	Yes	Limited	No	Yes
Prefactor	Runtime control plane	No	Yes	Limited	Partial	Yes
DashClaw	Runtime control	No	Yes	Free (self-hosted)	Partial	No (self-hosted)
AgentControl.dev	Control plane	No	Yes	Free (open-source)	Partial	No (self-hosted)
Microsoft Agent Governance Toolkit	CLI / policy definition	No	Partial (Azure-first)	Free (DIY)	Partial	No (DIY)
Peta.io	MCP control plane	No	Partial (MCP only)	Limited	Yes (MCP-focused)	Yes

A few patterns stand out from this comparison. The enterprise IAM vendors (Okta, Oasis, Astrix) are well-positioned for organizations where the CISO is the primary buyer and identity management is the core concern — but they don't give agents the tools to actually do meaningful work. The open-source tools (DashClaw, AgentControl.dev) are viable if your team has the capacity to operate them; for most product teams, that's a non-trivial ongoing tax. For a deeper look at how Handler compares to some of these vendors specifically, the 2026 AI agent governance platforms buyer's guide is a useful companion to this RFP framework.

How to Structure Your RFP Process: A Four-Phase Approach

Phase 1: Internal Requirements Gathering (Week 1–2)

Before you talk to any vendor, document your requirements across three dimensions: technical (framework, scale, integration targets), operational (managed vs. self-hosted, on-call burden), and compliance (audit requirements, data residency, SOC 2 expectations). Get sign-off from both the engineering lead and the security/compliance lead before proceeding. Misalignment here is the most common reason RFPs stall after vendor selection.

Phase 2: Long List to Short List (Week 3–4)

Use the eight criteria above to build a scoring matrix. Populate it from public documentation, not vendor-provided marketing decks. Any vendor that doesn't have detailed public documentation for developers should be treated with skepticism — it often means the product isn't mature enough for self-serve evaluation. Aim to cut from 10+ vendors to 3–4 based on this phase.

Phase 3: Technical Proof of Concept (Week 5–8)

Run each shortlisted vendor through an identical PoC scenario. A good PoC scenario for agent governance: build a simple agent that can send emails, query a database, and browse the web — then verify that you can (a) set rate limits on email sending, (b) require human approval before any database write, and (c) get a complete audit log of all actions taken. Time how long setup takes. Count the number of lines of config required. These are real metrics.

Handler, for example, lets you get a governed agent running with a single API key and an MCP server connection — you can try Handler free to validate the PoC timeline yourself. The $30/month Basic plan includes $30 in usage allowance, so there's no procurement process standing between you and a working test environment.

Phase 4: Reference Checks and Commercial Negotiation (Week 9–10)

Ask every finalist for two references: one engineering team and one security/compliance contact. The engineering reference will tell you about operational pain; the compliance contact will tell you whether the audit logs actually hold up under scrutiny. On commercial terms, pay attention to what happens when you exceed usage tiers — surprise overage fees on action-heavy workloads are a common gotcha in this category.

Red Flags to Watch for During Vendor Evaluation

Beyond the criteria above, these specific patterns should raise concerns during an AI agent governance RFP:

• No self-serve trial. If you can't test the product without a sales call, the developer experience is probably not a priority for that vendor.
• Governance defined as "prompt filtering." Filtering prompts does not govern agent actions. An agent that receives a clean response can still execute a destructive downstream operation.
• MCP as the only integration path. MCP is important, but agents also use REST APIs, OAuth connections, and direct SDK calls. A governance platform that only covers MCP has significant blind spots.
• Audit logs that are network-level only. Packet-level logs don't tell you what an agent decided to do or why. You need action-semantic logs.
• No published SLA for the control plane. If the governance layer goes down, what happens to your agents? If the vendor can't answer this clearly, that's a critical gap.

For organizations specifically worried about the identity and access side of agent security, our comparison of Okta AI agent governance alternatives covers how identity-first approaches differ from operation-level governance in practice.

Frequently Asked Questions

What is the difference between AI agent governance and AI agent security?

Security typically refers to protecting the agent from external threats — prompt injection, credential theft, unauthorized access. Governance refers to controlling what the agent is allowed to do even when operating normally. Both matter, but governance is the harder problem because it requires defining intent and acceptable behavior at the operation level, not just at the perimeter.

How many vendors should we include in an AI agent governance RFP?

Start with a long list of 8–12 vendors gathered from analyst coverage, peer recommendations, and direct searches. Use public documentation and the criteria in this guide to cut to 3–4 for technical evaluation. Running more than four through a full PoC is usually not worth the time — the differentiation between vendors 4 and 8 on your list is rarely actionable.

Should we build our own agent governance layer instead of buying?

Building a basic policy enforcement layer is achievable — it's essentially middleware that inspects tool calls and checks them against a ruleset. The harder parts are credential management, audit log infrastructure, human-in-the-loop approval workflows, and keeping up with MCP and A2A protocol changes. Most teams that start building internally end up with a half-finished system that covers their current agent but doesn't scale to new agents or frameworks. Buying a mature platform almost always has better ROI unless your use case is genuinely unusual.

How do we evaluate vendors if our agent framework isn't finalized yet?

Prioritize framework-agnostic vendors. Any platform that requires you to use a specific agent framework — or that works best with one — is creating lock-in that will cost you later. Specifically, look for vendors that expose both an MCP server interface and a REST API, so that any current or future framework can integrate without re-architecting.

What compliance standards should an AI agent governance platform support?

At minimum: SOC 2 Type II for the vendor's own posture, and the ability to produce audit logs that satisfy your organization's internal compliance requirements. If you're in a regulated industry, check specifically for data residency options (EU data staying in EU, for example), and ask whether the vendor has completed any AI-specific compliance frameworks like ISO/IEC 42001. The EU AI Act classifies some agentic AI systems as high-risk, which triggers additional audit and documentation requirements — your governance platform needs to support those if relevant to your deployment context.