I recently developed several AI Agents and was surprised by how well they performed. Want to share 10 important lessons I learned while building agents that actually work:
Build tools before everything else. Create and thoroughly test your tools before integrating with LLMs. Tools are the most predictable component of your system. Ensure they function perfectly before moving to agent development.
Begin with simple, foundational tools. Something like bash commands can handle most requirements effectively. No need to create dozens of specialized tools right away.
Use one agent initially. After building your core tools, test everything with a single react agent. Most agent frameworks include react agents by default, you just connect your tools.
Choose top-tier models first. Your system will have enough challenges without model limitations. Go with Claude Sonnet or Gemini Pro initially. Cost optimization can come later.
Monitor everything your agent does. Building agents feels like conducting experiments with unpredictable outcomes. Detailed monitoring is essential. Tools like Langsmith and Langfuse help with this.
Find what slows you down. Sometimes a basic agent with simple tools works perfectly. If not, check your logs to find issues. Common problems include long context windows, generic tools, or model knowledge gaps.
Fix problems systematically. Many improvement options exist like multi-agent systems, better prompts, or specialized tools. Pick solutions that address your specific bottlenecks.
Mix workflows with agents when it makes sense. For specialized tasks with clear sequences, workflows work better. Each workflow step can include an agent. Example: research agents work well as two-step workflows with broad search followed by focused report generation.
Pro tip: Use file system creatively. Files help AI Agents store information, remember context, and share data efficiently. Passing file paths instead of full content saves significant context space.
Pro tip: Learn from Claude Code. Claude Code represents current best practices in agent design. Even though it’s proprietary, CC understands prompting, architecture, and tooling well enough to provide guidance for your projects.
totally agree! #6 hit home for me too. I struggled with my agent till i checked the logs and realized my tools were too basic. switching to the right ones made a world of difference. and yes, the file system trick is a game changer for saving context!
Great insights. Here’s what I learned the hard way - track your prompt versions like your life depends on it. I wasted weeks debugging issues that were just undocumented prompt tweaks. Now I version control everything and A/B test changes systematically. Also, explicit error handling in prompts is a game changer. My agents jumped from 70% to 95%+ reliability just by telling them exactly what to do when stuff breaks. There’s a huge difference between ‘try to search for information’ and ‘if search returns no results, acknowledge this and suggest alternatives.’ Specificity kills random failures.
The workflow mixing point hits home. I’ve been doing this for months and it’s amazing how much more reliable everything gets when you mix structured workflows with agent flexibility.
But the real game changer? Automating the whole agent deployment and monitoring setup. Instead of manually wiring up tools and connections every time, I built workflows that do the heavy lifting - deploy agents, hook up monitoring, manage files, even auto-scale based on performance.
You can template what works and spin up new agents instantly. No more rebuilding the same monitoring setup or recreating connections from scratch. Just clone a proven template and tweak it for your needs.
This killed most of my deployment headaches and lets me focus on actual agent logic instead of infrastructure babysitting. Plus automated monitoring catches issues way faster than I ever could.
If you’re serious about scaling agent development, automation isn’t optional. Check out https://latenode.com
Point 4 hits hard. Wasted two weeks trying to force GPT-3.5 into complex reasoning before switching to Claude Sonnet. Night and day difference.
Biggest lesson though - make your agent architecture model agnostic from the start. Learned this the hard way when Claude’s API went down last quarter and killed my entire system.
Now I route by task complexity and availability. Cheap models handle simple classification, premium ones get the complex reasoning. If one’s down, it auto-falls back.
Your file system point is dead on. I take it further with structured naming - agent_id_task_timestamp.json instead of random temp files. Makes debugging way easier when you can trace exactly which agent made what and when.
Last thing - test edge cases early. I optimized for happy paths forever, then watched everything break on malformed inputs and API timeouts.
the monitoring part is huge. i built my first agent with zero logging and it was a nightmare - couldn’t debug anything. now i log every tool call, response, and decision. it’s saved me countless times when clients complain about weird behavior - i can trace exactly what broke and when. also keep your context windows tight. even good models hallucinate when you dump too much info on them.
Wish I’d learned this sooner - keep your tool outputs consistent. I spent hours debugging weird agent behavior, only to find one tool returned JSON while another sent plain text. The model got confused switching formats mid-conversation. Once I made everything return structured JSON with status codes, the agent became way more predictable. Also, set timeouts on every tool call. I had sessions hang for minutes because one slow API call locked up everything. Basic timeout handling turned my flaky tools into reliable ones.