The AI agent hype cycle is in full swing. Every pitch deck mentions autonomous agents, every product roadmap includes an 'agentic workflow,' and every engineering team is experimenting with tool-calling LLMs. But after helping dozens of teams ship agent-based features, we've learned that agents are powerful when used correctly — and catastrophically expensive when used where a simpler pattern would suffice.
When Agents Actually Make Sense
Agents shine when you have tasks that require multi-step reasoning with branching logic, access to multiple tools or data sources, and where the exact sequence of steps can't be predetermined. Think: research workflows, complex data analysis, or multi-system orchestration.
They don't make sense when you have a well-defined input-output mapping (use a simple chain), when latency matters more than flexibility (agents are slow), or when the task requires near-100% reliability (agents are probabilistic).
Warning
Every autonomous decision an agent makes is a potential failure point. A 5-step agent with 95% accuracy per step has a 77% end-to-end success rate. At 10 steps, it drops to 60%. Build deterministic guardrails around every agent workflow.
Three Architecture Patterns That Work
After shipping agents across a wide range of products, we've converged on three reliable patterns:
1. Supervised Agent — The agent proposes actions but a human approves each step. Best for high-stakes workflows like financial operations or content publishing.
2. Constrained Agent — The agent operates freely within a bounded action space. You define the tools, the maximum number of steps, and the fallback behaviour. Works well for customer support and data processing.
3. Orchestrator + Specialists — A lightweight agent routes tasks to specialised, deterministic sub-systems. The agent handles intent classification and routing, but the heavy lifting is done by reliable, tested code paths.
Observability Is Non-Negotiable
You cannot ship an agent without comprehensive logging of every decision, tool call, and intermediate result. We use structured logging with correlation IDs that let us replay any agent execution step-by-step. When something goes wrong — and it will — you need to understand exactly which step failed and why.
We also track token usage per agent run. Unoptimised agents can cost 10-50x more than necessary because they make redundant tool calls or over-fetch context.
Testing Agents Is Hard, But Essential
Unit testing individual tools is straightforward. Testing agent behaviour is not. We use a combination of:
Conclusion
AI agents are a powerful tool — emphasis on tool. Use them where the problem genuinely requires adaptive, multi-step reasoning. Everywhere else, a well-designed chain or a deterministic workflow will be more reliable, cheaper, and easier to debug.
Written by
Head of AI at Fyutrex
Priya leads AI engineering at Fyutrex, specialising in LLM integration, RAG pipelines, and intelligent automation for production products.
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