How to Choose an AI Agent Platform: A Buyer's Checklist
Mentiko Team
The AI agent platform market is crowded and confusing. Frameworks call themselves platforms. Platforms call themselves frameworks. Everyone claims to be "production-ready."
Here's a practical checklist for cutting through the noise and evaluating what actually matters for your team.
The 12-point evaluation checklist
1. Architecture: Event-driven or graph-based?
Event-driven (Mentiko): Agents communicate through events. Loose coupling. Easy to add, remove, or swap agents without rewiring the system.
Graph-based (LangGraph, most frameworks): Agents connected by explicit edges in a state machine. Tight coupling. Changes require updating the graph definition.
Ask: Can I swap an agent without changing the orchestration logic?
2. Language support: Python-only or polyglot?
Most platforms are Python-only. If your team works in TypeScript, Go, or Rust -- or if your agents need to run CLI tools -- this is a dealbreaker.
Ask: Can my agents be anything that runs in a terminal, or must they be Python code?
3. Execution model: Sandboxed or real environment?
Sandboxed: Agents run in a restricted environment. Safe but limited. Can't access the filesystem, can't run git commands, can't install packages.
Real environment: Agents run in PTY sessions with full access to tools. Powerful but requires trust in the agent.
Ask: Can my agents run git push, terraform apply, or npm test?
4. Pricing: Per-execution or flat-rate?
Per-execution pricing punishes experimentation and scales poorly. Flat-rate pricing is predictable and encourages iteration.
Ask: What does my bill look like at 100 runs/month? At 10,000?
5. Data residency: Their servers or yours?
If your agents process customer data, code, or API keys, where does that data live?
Ask: Do my API keys transit your servers? Where is my data stored? Can I choose the region?
6. Monitoring: Built-in or build your own?
Real-time monitoring of multi-agent workflows is essential for production. If the platform doesn't include it, you're building a dashboard from scratch.
Ask: Can I see which agent is running right now? Can I see per-agent logs and timing?
7. Scheduling: Built-in or external?
Running agent chains on a schedule is table stakes. If you need to set up Celery, Cloud Functions, or cron jobs yourself, the platform is incomplete.
Ask: Can I schedule a chain with one line of configuration?
8. Error handling: Automatic or manual?
Agents fail. Models hallucinate. APIs timeout. The platform should handle retry logic, fallback agents, timeout detection, and human escalation.
Ask: What happens when an agent fails mid-chain? Can I define fallback behavior?
9. Multi-tenancy: Built-in or retrofit?
If multiple teams or customers will use the system, data isolation must be designed in from the start. Retrofitting multi-tenancy is one of the most expensive engineering projects.
Ask: Can different teams have isolated workspaces with role-based access?
10. Secrets management: Encrypted or plain text?
Agent chains need API keys, database credentials, and tokens. If the platform doesn't have encrypted secrets management, you'll either hardcode secrets (dangerous) or build your own vault (expensive).
Ask: Are secrets encrypted at rest? Are they injected at runtime or stored in chain configs?
11. Portability: Open formats or lock-in?
Can you export your chain definitions and take them elsewhere? Or are they stored in a proprietary format on the vendor's servers?
Ask: Can I git-commit my chain definitions? Can I export everything and leave?
12. Visual builder: Available or code-only?
Not everyone on the team writes code. A visual chain builder lets non-engineers understand, modify, and create workflows.
Ask: Can I drag-and-drop agents into a chain, or must I write JSON/Python?
Quick comparison
| Criteria | Mentiko | CrewAI | LangChain/LangGraph | AutoGen | |---|---|---|---|---| | Architecture | Event-driven | Crew-based | Graph-based | Conversational | | Language | Any (PTY) | Python | Python | Python | | Execution | Real PTY | Sandboxed | Sandboxed | Sandboxed | | Pricing | Flat-rate | Per-execution | Free + LangSmith | Free | | Data residency | Your server | Their cloud | Your code | Your code | | Monitoring | Built-in | Limited | LangSmith | None | | Scheduling | Built-in | External | External | External | | Error handling | Automatic | Basic | Manual | Manual | | Multi-tenancy | Built-in | No | No | No | | Secrets | AES-256 vault | Manual | Manual | Manual | | Portability | JSON files | Python code | Python code | Python code | | Visual builder | Yes | No | No | No |
The decision
If you're building a one-off agent script: use a framework (LangChain, CrewAI).
If you're running multiple agent workflows in production with a team: use a platform (Mentiko).
The distinction is between a library you embed in your code and a platform that runs your workflows.
Ready to evaluate Mentiko? See the full comparison or try it in 5 minutes.
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