Choosing an AI Agent Platform

The Build vs. Buy Decision

This is the first fork in the road. No-code platforms (Zapier AI, Make, n8n with AI nodes) let you deploy agents in hours using visual builders and pre-built connectors. Code-first frameworks (LangChain, CrewAI, AutoGen) give you full control over the agent loop, tool registry, and output validation. Custom builds on serverless infrastructure (Cloudflare Workers, AWS Lambda) offer maximum flexibility but require the most engineering investment.

The deciding factor is edge case density. If your workflow has 3 to 5 predictable paths, a no-code platform handles it well. If your workflow has 20+ branching conditions, external API dependencies, and needs custom error recovery, code-first is the better investment.

Five Evaluation Criteria

• Integration breadth: Does the platform connect to your CRM, email provider, calendar, accounting system, and other tools natively? Every missing integration is custom development work.
• Reliability: How does the platform handle failures? Look for automatic retries with exponential backoff, step-level recovery (resume from where it failed, not from the start), and structured error reporting.
• Observability: Can you see what the agent did, which tools it called, what data it processed, and why it made each decision? Without this, debugging production issues is guesswork.
• Cost structure: Understand the full cost model. Platform fee plus LLM token costs plus external API call costs plus storage. Model your expected volume and calculate total monthly cost, not just the sticker price.
• Customizability: Can you define custom tools, modify the agent's reasoning loop, add validation rules, and control output formats? Templates get you started; customization keeps you running.

Architecture Considerations

Agent platforms differ in how they handle state, concurrency, and multi-agent coordination. Stateless platforms execute each request independently, which is simpler but cannot handle multi-step workflows that span minutes or hours. Stateful platforms maintain conversation history and task progress, enabling long-running workflows but adding complexity.

For multi-agent systems, look at how the platform handles agent-to-agent communication. Orchestrator patterns (one coordinator dispatching to specialists) are more predictable than peer-to-peer patterns (agents negotiating directly). The orchestrator approach is easier to debug and monitor.

Common Mistakes

Three patterns that consistently lead to platform regret. First, choosing based on demo impressiveness rather than production reliability. A slick demo does not mean the platform handles errors well at 3 AM. Second, underestimating LLM costs. Token usage scales with agent complexity, conversation length, and retry frequency. Third, ignoring vendor lock-in. If your agents are tightly coupled to one platform's proprietary tool format, migration becomes a rewrite.

See Different Agent Architectures

These demos illustrate different agent patterns: single-agent, multi-agent orchestration, and tool-calling pipelines.