This is why your AI Agent Fails, Context Engineering

Albin Xavier

Tue, 01 Jul 2025

what’s this about?

Context engineering is about giving AI agents what they actually need to work.

Not just prompts. Not just documents. Real, useful context.

It’s like this:

If you ask someone to fix a car but you don’t tell them which car, what the problem is, or what tools they can use, they’ll fail.

That’s what’s happening with most AI agents today.

what is context engineering?

It’s a way to build the right environment around your AI.

You don’t just send a question. You also send the files, the system state, the user’s last action, and the tools the agent can use.

This is what most people miss.

Context engineering means you:

• Track what files are open
• Know what the user just did
• Understand system logs and errors
• Keep the user’s goal in focus
• Connect the right tools for the agent to use

It’s like giving the agent a real map, not just a random note.

why does this matter?

Even if your retrieval is good, even if your agents plan well, even if your model is strong

if you don’t give the right context, the agent will still fail.

Here’s what usually goes wrong:

• The agent works on the wrong file.
• It pulls old or useless information.
• It can’t run tests or search because you didn’t connect tools.
• It repeats steps the user already did.

This happens all the time.

It’s not the model’s fault.

It’s usually a context problem.

how context engineering helps

When you do it right:

• The agent knows what to do.
• It knows where to look.
• It knows what tools to use.
• It knows when things change.

That’s how you build agents that actually work.

what’s next?

I’m starting a series where i’ll:

• break this down step-by-step
• show real examples
• share how i’m building this with langgraph and other tools

i’ll post updates here and on linkedin.

if this is something you’re working on or thinking about, keep checking back.

you can also follow me on linkedin to stay updated.

i’ll keep it practical. i’ll show what works.

see you in the next one.