AI Agent Platform Checklist: What to Verify Before You Commit

How to Use This Checklist

Work through each section against every platform you are seriously evaluating. Where the checklist says “ask the vendor to demonstrate,” treat a description as an incomplete answer — the capability should be visible in a live environment before you commit. Where it says “verify in documentation,” treat a verbal assurance as insufficient — the detail should exist in writing.

The checklist is organized by feature area across the AI agent lifecycle — from workflow design through to compliance and support. This checklist assumes familiarity with these feature areas and focuses on how to verify them in practice. For a conceptual overview of why each feature area matters, see AI Agent Platform Features: What to Look For.

1. Workflow Design and Logic (Behavior Definition)

The workflow builder determines how precisely you can define agent behavior — and how much of your real workflow complexity the platform can actually handle.

Verify:

• The platform supports conditional branching on three or more simultaneous conditions — not just linear flows with basic yes/no splits
• Workflows can be updated in production without full redeployment
• Exception handling — what the agent does when input falls outside expected parameters — can be explicitly defined, not just defaulted
• Multi-agent workflows are supported if your use case requires more than one agent coordinating on a task
• Workflow templates exist for your primary use case and are genuinely customisable, not cosmetically adjustable

Ask the vendor to demonstrate: A workflow that branches on three or more conditions simultaneously, hits an exception mid-flow, and resolves it without human intervention. If the demo uses a simpler flow, ask why.

2. Knowledge Base and Grounding (Answer Accuracy)

Grounding quality — how accurately and consistently the agent draws from your content — determines whether the agent is useful or merely fluent.

Verify:

• The platform ingests your content formats — PDFs, URLs, structured data, internal documentation — without requiring reformatting
• The agent retrieves accurately when questions are phrased differently from how the source material is written
• The agent acknowledges the boundary of its knowledge rather than generating confident-sounding answers outside its scope
• Knowledge base updates propagate to the agent without redeployment delay
• Source attribution is available — the agent can indicate where an answer came from, which matters for regulated industries

Ask the vendor to demonstrate: Upload a document from your own content library. Ask the agent three questions answered in the document but phrased nothing like the source text. Then ask one question the document does not answer. Assess accuracy on the first three and boundary behavior on the fourth.

3. Memory Architecture

Working memory and long-term memory are distinct capabilities with different implications for workflow continuity. Conflating them is one of the most consequential evaluation errors in this category.

Verify:

• Working memory — context within a single session — is confirmed and tested against a multi-turn workflow
• Long-term memory — context persisting across sessions — is confirmed with a specific demonstration, not a verbal description
• The platform specifies what is stored in long-term memory, for how long, and in what form the agent retrieves it
• Memory can be cleared or modified for specific users on request — relevant for compliance and data subject rights
• Memory behaviour under concurrent users is documented — what happens when many users are active simultaneously

Ask the vendor to demonstrate: Start a workflow, end the session, return after a defined interval — hours if possible, not seconds — and verify what the agent knows about the prior interaction. This single test surfaces more about memory architecture than any documentation review.

4. Action Layer and Integrations

The action layer defines what the agent can actually do. Evaluating it on availability rather than reliability is the most common procurement error in this category.

Verify:

• Every integration your workflow requires is confirmed as native — not reliant on custom middleware your team would own
• The agent’s behavior on integration failure is documented and demonstrated: does it retry, route around the failure, escalate with context, or stall?
• Integrations write structured data to connected systems in the correct format — not free text requiring manual handling downstream
• API rate limits on connected systems are handled gracefully — the agent does not fail silently when a rate limit is hit
• New integrations can be added without rebuilding the core workflow

Ask the vendor to demonstrate: Simulate an integration failure mid-workflow — ask what happens when the CRM, scheduling system, or data source the agent depends on returns an error. If the vendor cannot demonstrate this, it has not been designed for.

5. Human Handover and Escalation

Handover quality is the feature most commonly underevaluated and most consistently consequential in production.

Verify:

• Escalation triggers are configurable at the workflow level — not just platform-wide defaults
• The receiving human sees: full conversation history, structured data collected by the agent, current workflow state, and reason for escalation
• Escalation can be triggered by both explicit signals (user request) and implicit ones (confidence below threshold, tool failure, defined time limits)
• Post-escalation workflow — what happens after the human resolves the issue — is defined and tested
• Escalation events are logged with timestamps and context for audit purposes

Ask the vendor to demonstrate: A live handover mid-workflow. Assess exactly what the receiving human sees — not what the vendor describes, but what appears on screen at the moment of transfer.

6. Communication Infrastructure Integration

Some AI agent platforms operate as standalone systems that connect to communication channels via integration. Others include native chat, video, and messaging capabilities within the same infrastructure as the AI agent layer — meaning the agent and human communication channels share context by default, operate under the same compliance agreement, and allow seamless handoff without platform switching.

Understanding which model a platform uses — and whether the integrated model is available and relevant for your workflow — is worth establishing early in evaluation.

Verify:

• Whether the platform includes native chat, video, and messaging capability alongside AI agent functionality — or whether these require separate integration
• Whether context is shared natively between the AI agent and human communication channels — or whether it must be transferred via custom integration
• Whether a single compliance agreement covers both the AI agent layer and the communication infrastructure
• Whether the agent can initiate or escalate to a video consultation directly — without routing through a separate platform
• Whether communication channel data — chat history, call logs — is available to the agent as context for ongoing workflows

Ask the vendor to demonstrate: A handoff from AI agent interaction to a live video or chat session, with full context visible to the human agent at the point of transfer. If this requires leaving the platform or switching interfaces, the integration is not native.

7. Analytics and Performance Monitoring

The analytics capability of a platform determines how quickly you can identify and fix problems after deployment — and how much ongoing visibility you have into agent performance at scale.

Verify:

• Workflow-level drop-off analysis is available — showing where in a specific workflow users abandon or require escalation, not just aggregate completion rates
• Tool call success and failure rates are logged and reportable by integration
• Input classification data is available — which query types the agent handles well and which it handles poorly
• A/B testing or staged rollout capability exists for testing workflow changes before full deployment
• Dashboards are accessible to non-technical users — not only via API or developer tooling

Ask the vendor to demonstrate: A live analytics view from an existing deployment. Specifically ask to see where a workflow fails most frequently and what the platform surfaces to help diagnose the cause.

8. Security and Compliance

Security and compliance evaluation varies significantly by industry. The baseline applies universally; the healthcare-specific items apply to any deployment handling protected health information.

Verify — baseline:

• Data encrypted in transit and at rest — confirm encryption standards, not just the presence of encryption
• Role-based access controls are available — limiting who can view, edit, and deploy agent workflows
• SOC 2 Type II certification is current — request the report, not just a claim
• Data residency options are available if your deployment requires data to remain in a specific jurisdiction
• Penetration testing results are available on request under NDA

Ask the vendor to demonstrate: Access control configuration and audit logging in a live environment — specifically who can access workflow data, how changes are tracked, and what visibility exists into agent activity. For regulated deployments, request documentation of compliance coverage across the full stack, not just the hosting layer.

For a deeper breakdown of security and compliance considerations across deployments, see AI Agent Security and Compliance.

9. Deployment and Scalability

Deployment determines how quickly the agent moves from evaluation to production — and scalability determines whether it continues to perform as usage grows. Both are often assessed at a surface level, but mismatches here are what most commonly delay go-live or degrade performance under real-world load.

Verify:

• Deployment options match your infrastructure requirements — cloud, on-premises, or hybrid
• The platform handles concurrent user load at your expected scale — request performance benchmarks, not estimates
• SLAs covering uptime and response time are available in writing — and penalties for breach are defined
• The platform supports multiple agents across different use cases managed from a single interface
• Onboarding support is included — and the scope of what is included versus billable is clearly defined

Ask the vendor to demonstrate: A live or recorded example of the platform operating at scale — including concurrent users and multiple active workflows. Request evidence of performance under load, not just stated benchmarks, and clarity on how the system behaves as usage increases.

10. Pricing and Commercial Terms

Pricing determines the long-term viability of a deployment, not just its initial cost. Understanding how costs scale with usage — and what constraints exist within the commercial model — is as important as understanding the platform’s technical capabilities.

Verify:

• Pricing structure is understood at your expected usage volume — per conversation, per agent, per seat, or platform fee
• Usage caps and overage charges are defined — and the cost of exceeding them at 2x your projected volume is calculated
• Contract terms allow for workflow changes and agent additions without renegotiation
• A free trial or pilot period is available before full commitment — and what access it includes is confirmed in writing
• Data ownership terms are explicit — your content, conversation data, and workflow logic remain yours

Ask the vendor to demonstrate: A pricing model based on your expected usage — including projected costs at baseline, at 2x volume, and under peak conditions. Ask the vendor to walk through a real billing scenario so that cost drivers are fully visible before commitment.

The QuickBlox Perspective

The two items on this checklist that procurement processes most consistently skip — and that production performance most consistently reveals — are communication infrastructure integration and action layer failure behavior.

First, communication infrastructure is treated as an assumption rather than an evaluation criterion. Most teams assume their AI agent will connect to their communication stack and evaluate that connection only after deployment, when the integration complexity becomes visible. The question of whether the AI agent and the communication layer — chat, video, messaging — share context natively, operate under the same compliance agreement, and allow seamless handoff without platform switching should be on the evaluation checklist from day one. For deployments where the agent hands off to a human on video or chat, this is not a nice-to-have — it is the handoff architecture.

Second, failure behavior is evaluated last and should be evaluated first. Every platform on your shortlist will handle your primary workflow cleanly when inputs are well-formed and tools are working. The evaluation that predicts production performance is the one that tests what happens when they are not. Run the failure scenarios in this checklist before you run the success scenarios. What you see will reorder your shortlist more reliably than any feature matrix comparison.

QuickBlox AI Agents are built on QuickBlox’s communication infrastructure — meaning chat, video, and file sharing are not integrations to be configured but native capabilities that the AI agent layer operates alongside from deployment. For healthcare teams, this means a single BAA covering the agent, the communication layer, and the hosting environment — and a handoff architecture that carries full context from AI agent to video consultation without leaving the platform. If you’re working through this checklist against specific platforms and want to compare notes on what you are finding, we’re happy to think it through with you.