AI Agent Platforms 2025: Build vs Buy Guide for Series B+ Startups

We evaluated 12 AI agent platforms before building our own. Here's what we learned about build vs buy.
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The short answer: most platforms solve the wrong problem.

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The platform landscape (Early 2025)
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The market is exploding with AI agent platforms:

• No-code builders: AutoGen Studio, Vertex AI Agent Builder, n8n
• ‍Developer frameworks: LangChain, CrewAI, AutoGPT
• ‍Enterprise platforms: Salesforce Agentforce, Microsoft Copilot Studio
• ‍Specialized tools: QA agents, sales agents, support agents
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All promise the same thing: "Build AI agents in minutes, not months."

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Most fail to deliver for Series B+ companies. Here's why.

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What these platforms get wrong
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1. They optimize for demo speed, not production reliability
   ‍Building a working prototype takes minutes. Getting it production-ready takes months. The platforms hide this complexity.
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2. They assume simple workflows
   ‍Most platforms work great for "when X happens, do Y" logic. They break down when you need complex reasoning, multi-step planning, or adaptive behavior.
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3. They don't integrate with your existing stack
   ‍Generic platforms don't know your data model, your APIs, or your business logic. Integration becomes your problem.
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4. They lack enterprise-grade monitoring
   ‍When your agent fails in production, you need detailed logs, error tracking, and alerting. Most platforms give you basic dashboards at best.
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What we built instead (And Why)
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At Islands, we built our own agent infrastructure. Not because we love reinventing wheels, but because the trade-offs matter.
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Our stack:

• Orchestration: Temporal (workflow management)
• ‍LLM layer: Direct API calls to OpenAI/Anthropic (flexibility)
• ‍State management: PostgreSQL (our data, our control)
• ‍Monitoring: Custom dashboards + Datadog (visibility)

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Why this works for us:

• Complete control over cost optimization
• Deep integration with our products
• Custom monitoring for our use cases
• No vendor lock-in

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The cost: Higher upfront engineering investment. Worth it for our scale.

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When to use platforms

Platforms make sense when:

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You're experimenting (Pre-Series A)

‍Use AutoGen or n8n to validate the concept. Speed matters more than control.

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You have simple workflows

‍If your agent logic is straightforward, platforms work great. Example: "When support ticket comes in, categorize and route it."

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You lack AI engineering resources

‍Platforms reduce the ML expertise needed. Your existing engineers can build agents.

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You need fast time-to-value

‍If you're racing to add AI features, platforms get you there faster.

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When to build custom

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Build your own when:

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You're scaling (Series B+)

‍Volume drives cost. Custom infrastructure lets you optimize. We've seen 10x cost reductions.

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You have complex workflows

‍Multi-step reasoning, adaptive behavior, and state management favor custom solutions.

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You have AI engineering talent

If you can hire ML engineers, they'll be more productive building than configuring.

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Reliability is critical

‍Production systems need robust error handling, monitoring, and recovery. You get this with custom builds.

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The hybrid approach (our recommendation)

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Most successful companies do both:

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Start with platforms for:

• Rapid proto typing
• Non-critical workflows
• Learning what agents can do

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Build custom for:

• Core product features
• High-volume workflows
• Mission-critical automation

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Specific platform recommendations

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Best for experimentation: AutoGen Studio (free, fast, good for learning)

‍Best for developers: LangChain (most flexible, huge community)

‍Best for enterprise: Vertex AI Agent Builder (Google-backed, good integrations)

‍Best for sales teams: Clay + GPT-4 (not technically an agent platform, but works)

‍Best for support: Intercom's Fin (purpose-built, works out of box)

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What to evaluate

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When assessing any platform, test these:

1. Error handling
   ‍Break it intentionally. How does it recover? What visibility do you get?
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2. Cost at scale
   ‍Calculate cost per 1,000 agent runs. Platform fees + LLM costs add up fast.
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3. Integration friction
   ‍How hard is it to connect your data? Your APIs? Your auth?
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4. Vendor lock-in
   ‍Can you export your agent logic? Switch LLM providers? Own your data?
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The bottom line
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For Series B+ startups, the answer is usually: start with platforms, build custom for what matters. Platforms let you move fast and learn. Custom infrastructure lets you scale and optimize.Don't overthink it. Pick a platform, build something, ship it. You'll know when it's time to rebuild.

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