Evaluating AI Orchestration Platforms: A Buyer's Checklist

How to use this checklist

This is the list of questions we wish more buyers asked us. Walking through it with any vendor — including AioClaw — will surface the difference between a polished demo and a platform that survives production.

Architecture (5 questions)

1. Is the platform model-agnostic, or locked to one LLM provider?
2. Can we bring our own agent code, or only configure the vendor's primitives?
3. How is context shared between agents — chat log, summary, or graph?
4. What runs serverless, what runs on persistent workers, and who pays for idle?
5. What's the upgrade path when a new model class (multimodal, reasoning, on-device) ships?

Observability (4 questions)

1. Can we trace a single user request across every agent, tool and model call?
2. Are token counts, costs and latencies attributed per task, not just per call?
3. Can we replay a failed run with the exact inputs that produced it?
4. Do logs, traces and metrics export to our existing stack (Datadog, OpenTelemetry, etc.)?

Cost controls (3 questions)

1. Can we set hard ceilings per agent, per user, per workflow?
2. Does the scheduler route to cheaper providers when latency allows?
3. What's the unit economics story — is pricing per seat, per token, per task, or per agent-hour?

Safety and audit (4 questions)

1. Can we enforce that certain actions require human approval before execution?
2. Is every tool call signed and immutable for audit?
3. What's the data residency model — where do prompts, outputs and embeddings live?
4. How are secrets and customer-scoped tokens isolated between tenants?

People and process (4 questions)

1. Can non-engineers ship a new agent, or does every change route through ML?
2. What's the time-to-first-agent-in-production for a new team?
3. How are agent regressions caught — eval suites, shadow runs, A/B?
4. What does the runbook look like when an agent misbehaves at 3am?

Red flags

• Demos that only work with the vendor's own LLM.
• "Cost optimization" that's really just a cheaper default model.
• No per-task tracing — only per-conversation.
• Human-in-the-loop that blocks the whole graph instead of one branch.
• Pricing that scales with agents but not with usage — or vice versa.