There’s a big gap between what AI agents promise and what engineering teams actually need.
Here’s a quick take on where most agents fail — and why context isn’t optional 👇
The pitch is familiar: “AI agents will handle the boring stuff so engineers can focus on the hard problems.”
But here’s the catch: the boring stuff is the hard problem.
Especially in large, complex codebases.
When agents hallucinate plans, break pipelines, or misunderstand intent, they don’t feel like assistants.
They feel like interns with root access.
And at scale, that’s not just annoying—it’s dangerous.
The issue isn’t just capability. It’s context.
Because writing code isn’t the hard part.
Keeping it aligned with everything else is.
In real engineering orgs, context means:
– Following architectural patterns
– Knowing what already exists
– Updating docs, tests, and CI together
– Avoiding subtle regression paths
– Understanding what not to touch
Most agents don’t do that. They operate like they’re starting from scratch.
But engineering rarely starts from scratch.
It lives in the messy middle — where context is everything and nothing is cleanly scoped.
You can’t bolt on context.
You have to build for it from the start.
Or the agent will always be guessing—and guessing breaks things.
AI agents have incredible potential. But potential without precision is risk.
And for teams managing million-line systems, trust matters more than speed.
The future of agents isn’t just about automation. It’s about awareness. And the real unlock is
contextual competence —not just code generation.
