Building an AI agent sounds simple — until you actually try to make one reliable.
Why I Started
Over the past few months, I’ve been working on designing an AI agent for a real-world use case.
Not a demo. Not something that works “most of the time.”
But something that could quietly support real business operations.
And very quickly, I realised:
The challenge is not making the agent respond.
The challenge is making it respond well, consistently, and usefully.
1. Memory Is Not What You Think It Is
At the start, I thought memory was straightforward.
Store past conversations. Retrieve them when needed. Let the model use it as context.
Simple.
But in reality, memory design is less about storage — and more about relevance.
Too little memory:
The agent feels forgetful Conversations lose continuity
Too much memory:
The agent becomes noisy Important signals get diluted Costs increase unnecessarily
The real question became:
What should the agent remember — and what should it forget?
And that turned out to be more of a product decision than a technical one.
2. Context Is a Balancing Act
Feeding context into an agent feels like giving it knowledge.
So naturally, the instinct is to give it more.
More documents More instructions More examples
But more context does not always mean better responses.
Beyond a certain point:
Responses become slower Quality becomes inconsistent The signal gets buried in noise
There is a subtle balance between:
Giving enough context to be helpful
vs
Keeping it lean enough to stay sharp
And this balance is not fixed — it evolves over time.
3. A “Smart” Agent Is More Expensive Than You Expect
When people think about AI cost, they often think in terms of:
“Cost per API call”
But that’s only a small part of the picture.
In reality, cost comes from:
Repeated interactions Long context windows Retrieval processes Iterations to improve quality
A slightly smarter agent often requires:
More tokens More processing steps More orchestration
Which shifts the question from:
Can we build this?
to
Can we sustain this at scale?
4. Specific vs General: The Trade-Off
One of the most interesting tensions is this:
The more specific the agent becomes, the more reliable it is.
The more general it becomes, the more flexible — but also less predictable.
A specialised agent:
Gives more accurate answers Has lower hallucination risk Feels more consistent
But:
Struggles outside its scope Requires more upfront structure
A general agent:
Handles broader conversations Feels more natural
But:
Is harder to control More prone to drifting
So the real design question becomes:
What should this agent be really good at — and what should it avoid?
What Changed My Thinking
Before building this, I thought designing an AI agent was mostly a technical challenge.
Now, I see it differently.
It is:
A product design problem A cost management problem A user experience problem And only partly a technical one
Because users don’t care how the agent works.
They care whether:
It understands them It responds clearly It actually helps
The Philosophy I’m Taking Forward
If there’s one thing I’ve learned so far, it’s this:
Start with usefulness, not intelligence.
A simpler agent that:
Solves a real problem Behaves consistently Communicates clearly
Is far more valuable than a “smart” agent that tries to do everything.
Closing Thought
We are still early in this space.
There are many tools, many frameworks, many approaches.
But beneath all of that, the core challenge remains surprisingly human:
What does it mean to be helpful?
And designing for that is where the real work begins.