Two years ago, building an AI agent meant assembling your own orchestration layer, writing prompt templates by hand, and praying your tool calling worked. Today in 2026, it's a commodity.
I've shipped 12 production agents in the last 8 months. Here's what I learned about templates, evaluation, and avoiding expensive mistakes.
Build vs. Use a Template
The question isn't "Can I build an agent?" It's "Should I?"
I built from scratch for:
- Custom domain logic
- Unique tool integrations
- Proprietary evaluation criteria
I used templates for:
- Retrieval-augmented generation (RAG)
- Customer support agents
- Internal documentation assistants
- Lead qualification chatbots
AgentKit saved me 16 hours on my fifth agent. It includes prompt templates, tool calling scaffolding, evaluation harness, and deployment configs.
Evaluating Agent Quality
I evaluate every agent across four dimensions:
1. Accuracy — Benchmark against gold-standard answers, measure semantic similarity
2. Latency — p50, p95, p99 response times
3. Cost — Cost per query (LLM API calls + token usage)
4. Safety — Does it refuse dangerous requests?
I run evaluation before every deployment. If accuracy drops below 90% or safety below 95%, I don't deploy.
Common Pitfalls
Pitfall 1: Using the wrong model
I tested Haiku for lead qualification. Accuracy was 73%. Switched to Sonnet—96% accuracy. The cost went up but paid for itself in one day.
Pitfall 2: Prompt injection
My support agent could be manipulated into revealing customer data. Solution: Separate user input from system context with explicit delimiters.
Pitfall 3: Hallucination at scale
An agent works great in testing, then produces garbage in production. Training data mismatch. Solution: sample 1% of production queries and manually verify accuracy weekly.
Pitfall 4: Runaway costs
I had an agent that retried failed tool calls infinitely. Added a max retry count and cost budget.
Template Comparison
| Dimension | Build from Scratch | Use Template |
|---|---|---|
| Time to deploy | 2-3 weeks | 2-3 days |
| Accuracy out of box | 60-75% | 85-95% |
| Cost to maintain | High | Low |
| Best for | Custom domain logic | Standard use cases |
Production Lessons
- Test with real data
- Monitor hallucination rate
- Version your prompts
- Budget for inference
- A/B test models
AI agents are solved problems for common use cases. The decision isn't whether to build—it's whether to template or customize.
What's your evaluation framework?
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