After 30 years of coding, I learned the secret to AI success: treat agents like a specialized team, not magic wands. Break problems down, write clear prompts, give them tools.
The story
Ever wonder why some people get incredible results from AI while others just get frustrated?
After thirty years of coding and eight years deep in AI, I’ve learned it’s not about the technology – it’s about treating AI agents like a team.
Here’s what actually works:
Break it down. One agent trying to do everything? Recipe for mediocrity. I split complex problems into clear subtasks and assign specialized agents – one searches the knowledge base, another reviews past tickets, a supervisor pulls it together. Just like a good human team.
Prompts are everything. Forget fancy language. What matters: clarity, context, specificity. I give agents complete context, test and refine constantly, and tune their “vibe” to match the task. Few-shot examples help when I need deeper reasoning. Think of it as training a new teammate – the clearer you are, the better they perform.
Give them tools. Agents aren’t all-knowing. I equip mine with document access for reference material, database connections, calculators, APIs. Each agent does what it’s best at, tools fill the gaps.
Manage them. I use dashboards to monitor activity, set rules for sensitive data, and review logs. Same oversight I’d give human staff. This builds trust and catches problems early.
Make it cultural, not just technical. Rolling out AI agents means helping your human team see them as force multipliers, not threats. Clear scopes, transparent supervision, and focusing on how people can add new value – that’s how adoption actually happens.
The discipline sits at the intersection of software architecture, prompt engineer
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