How to give an agent a goal it can actually follow
Most agents fail because the goal is written badly. Here is how to fix that.
It seems many people get stuck at the same point.
They build an agent.
They test it.
And it behaves in a strange way that makes no sense.
Most of the time, the problem is not the model.
It is not the tools.
It is not the memory or the loop.
It is the goal.
A vague goal creates a confused agent.
A broad goal creates an unpredictable agent.
A sloppy goal creates a messy loop.
They give you the foundation you need.
In this episode, we focus on the goal.
How to write it.
How to shape it.
How to make it so clear that the agent has no room to drift.
By the end of this, you will know how to write goals that guide the agent, support the loop, and reduce the chance of failure.
This is it.
Let’s get into it.
Why goals matter so much
An agent follows a loop.
Observe.
Think.
Act.
Reflect.
The goal is the thing that anchors the loop.
It tells the agent what success looks like.
It tells the agent when to stop.
It tells the agent which direction to move in.
If the goal is fuzzy, the loop becomes messy.
If the goal is clear, the loop becomes focused.
A good goal is not long.
It is not complex.
It is not technical.
It is simple.
It is measurable.
It is understandable.
What a bad goal looks like
Here are common examples:
• “Help me with my inbox”
• “Make this document better”
• “Organise everything for me”
• “Check my data and fix issues”
• “Handle my tasks”
These goals are too broad.
They hide the real outcome you want.
They give the agent no direction.
When the goal is unclear, the agent will guess.
And its guesses are often wrong.
What a good goal looks like
Here are the patterns you want:
✓ Clear
✓ Short
✓ Focused
✓ One outcome
✓ No hidden expectations
Examples:
• “Show me the five messages I must read today”
• “Find the dates that do not match in this dataset”
• “Sort these tasks into today, this week and later”
• “Summarise the risk points in this document in one paragraph”
• “Extract the vendor names and list them in a table”
Clear outcome.
Straight path.
No confusion.
Agents thrive when the goal is small and well defined.
The structure of a strong goal
Use this simple pattern:
Action + Target + Outcome
Examples:
• “Sort + these tasks + into three buckets”
• “Extract + the key findings + into a clean list”
• “Check + this dataset + for gaps in the dates”
• “Summarise + this report + in under one paragraph”
When the goal follows this shape, the agent knows exactly what the target is and what the result must look like.
Add constraints if they matter
Constraints are optional, but useful.
Examples:
• “Do not change the original text”
• “Keep the final answer under one hundred words”
• “Use these three categories only”
• “Do not run any external tools”
Constraints help shape the loop.
They give the agent boundaries.
They stop unnecessary actions.
You do not need many.
Just the ones that matter.
A quick example
Bad goal:
“Help me clean my inbox”
Better goal:
“Show me the five messages I must read today”
Even better with constraints:
“Show me the five messages I must read today.
Ignore newsletters.
Ignore promotions.
Ignore anything older than two weeks.”
This turns vague chaos into a clean task.
How to use this today
Before you build or test an agent, ask yourself:
• Is my goal clear
• Is it one outcome
• Can an agent act on it
• Does it follow action, target, outcome
• Does it need constraints
If the answer is no, rewrite it.
A strong goal will save you hours of debugging later.
Save this.
Good goal writing is half of agent design.
Up Next
Memory types and why agents forget.
In the next episode in this series, you will learn the difference between short-term memory, long-term memory and functional memory inside an agent, and how each one shapes the loop.
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