What I Learned Building AI Agents

After spending the last six months building AI agents at Gyld, I've made a lot of mistakes—and learned a lot of lessons. If you're building agents (or thinking about it), here's what actually matters.

1. Keep Your System Prompts Short

This one surprised me.

I used to think more instructions meant better performance. If the agent needed to handle edge cases, I'd add more rules. If it got something wrong, I'd add clarification. My prompts ballooned to thousands of tokens.

The result? Agents that got more confused, not less.

Here's what I've found: the longer your system prompt, the more likely your agent is to lose track of what matters. Important instructions get buried. The model starts contradicting itself. Responses get slower and more expensive.

What works better:

• Be specific, but concise. If you can't explain what the agent does in a few sentences, it's probably trying to do too much.
• Cut the edge cases. Handle them in code, not in the prompt.
• Test aggressively. A shorter prompt that works 95% of the time beats a long prompt that works 80% of the time.

I've seen agents improve dramatically just by cutting their system prompts in half.

2. Your Tools Need Crystal-Clear Definitions

Your agent is only as good as the tools you give it.

When an LLM decides which tool to use, it's reading the tool's name, description, and parameters. If any of those are ambiguous, the model guesses. And it guesses wrong more often than you'd think.

Bad tool definition:

Name: send_message
Description: Sends a message
Parameters: recipient, content

Better tool definition:

Name: gmail_send_email
Description: Send an email using Gmail. Requires recipient email address, subject line, and message body. Returns message ID on success.
Parameters: 
  - to (string, required): Recipient email address
  - subject (string, required): Email subject line  
  - message (string, required): Email body content

The difference is night and day. Clear tool definitions mean:

• Fewer wrong tool calls
• Better parameter formatting
• Less back-and-forth correction

Spend time on your tool descriptions. It's one of the highest-leverage things you can do.

3. Single Responsibility Wins

The best agents do one thing really well.

It's tempting to build a "super agent" that handles everything—email, calendar, CRM, Slack, you name it. But I've found that these Swiss Army knife agents underperform specialists.

Why? Because every additional capability:

• Adds more tools (making selection harder)
• Requires more context in the system prompt
• Creates more potential for confusion

Instead of one agent that does everything:

• Gary handles Gmail
• Oscar handles Outlook
• Quinn handles QuickBooks
• Simon handles Google Sheets

Each agent has a focused system prompt. Each has a small, relevant toolset. Each does its job well.

When you need cross-functional work, that's where routing comes in.

4. Smart Routing > Bloated Agents

When you need more capabilities, don't stuff them into one agent. Route to specialists instead.

Here's how it works: your Gmail agent receives a request that involves calendar scheduling. Instead of having calendar tools (which would bloat its toolset and confuse its purpose), it routes to your Calendar agent.

User: "Check my email and schedule a meeting with anyone who requested one this week."

Gmail Agent: 
  1. Searches emails for meeting requests
  2. Routes to Calendar Agent with: "Schedule meeting with John for Tuesday at 2pm"
  
Calendar Agent:
  1. Checks availability
  2. Creates the meeting
  3. Returns confirmation

Gmail Agent:
  4. Sends confirmation reply to John

Each agent stays in its lane. The Gmail agent doesn't need to know how calendars work. The Calendar agent doesn't need to understand email threading.

This architecture scales. Add a new integration? Create a new specialist agent. Your existing agents don't change.

The Bottom Line

Building good AI agents isn't about cramming more capabilities into a single system. It's about:

1. Clear, concise instructions that don't overwhelm the model
2. Well-defined tools that leave no room for guessing
3. Focused agents that do one thing exceptionally well
4. Smart routing that lets specialists collaborate

The companies winning with AI aren't building one mega-agent. They're building teams of focused agents that work together—just like a well-run company.

Want to see this in action? Check out the AI employees we've built at Gyld.ai.