The best AI agents aren’t coded — they’re conceived.
As an AI Solution Architect, I’ve seen teams rush into building with the same opening questions: “Should we use RAG?” “Which LLM is best?” “How do we build the API?”
These are important — but they’re not the first questions you should ask. The real asset isn’t the code — it’s the thinking behind the code.
Mindset first. Metal second.
Rethink the Agent: It’s Not a Standalone Bot
Most people imagine AI agents as “smart assistants” in isolation.
That’s a mistake. Every AI agent is part of a larger living system — your workflows, your teams, your tools, your risks.
Think like a system architect.
Real-World Example
A retail bank launched an AI FAQ bot. It worked — too well. Compliance couldn’t keep up with reviewing the hundreds of responses it generated weekly. The project paused, not for technical reasons, but because the agent had outpaced the system it lived in.
Systems Thinking in Action
Before coding, map how your agent fits into the ecosystem:
- Inputs and Outputs: Where does it get information? Who consumes the results?
- Interconnections: Does it depend on systems like CRM, Slack, or Data Lake?
- Human in the Loop: What happens when it fails? Who steps in?
- Unintended Consequences: What if it’s too successful?
Designing the agent is designing a new organ — and you need to understand the body it’s joining.
Context Is the Difference Between Smart and Annoying
A generic bot says, “How can I help?” A context-aware agent says, “Are you trying to return the sweater you received yesterday?”
That’s not clever prompting — it’s architected context.
Context is not just more data — it’s the right information at the right moment. Design for:
- Temporal context: What just happened?
- User context: Who is this?
- Task context: What are they trying to do?
- Environmental context: What device are they using? Where are they?
First Principles Over Feature Lists
Avoid building AI agents just because you can. Start with the actual problem.
Ask:
- Is this really an AI problem?
- Could a simpler tool (FAQ, UI improvement) solve it faster and cheaper?
- If AI is needed, what’s the smallest agent that provides real value?
Real-World Example
A B2B platform wanted an AI onboarding agent. After reevaluation, we replaced 80% of the journey with a guided UI. The AI was retained only for resolving edge cases — saving time and cost.
The Architect’s Mindset Is What Lasts
Frameworks change. Models evolve. APIs break. But a well-designed mental model will outlive them all.
Before writing a line of code, ask:
- Does this agent truly belong in the system?
- Have we mapped its context clearly?
- Are we solving the core problem, or just layering tech?