AI coding agents are getting better, but the annoying part has not disappeared.
You still paste the same project details. You still explain the same folder structure. You still remind the agent which framework version you use, where the issue came from, and what โdoneโ means in your repo.
That is not a model problem. That is a context problem.
The next useful shift for developers is simple: stop trying to make one giant perfect prompt. Build a small context system around the agent.
Give the agent the things a real teammate would ask for before touching code:
- the issue or task source
- the relevant docs
- the repo conventions
- the error logs
- the recent decisions
- the tests that prove the change works
This is why context engineering and MCP are becoming a big deal. MCP gives agents a standard way to fetch tools, files, docs, tickets, databases, and workflows instead of forcing developers to paste everything manually.
The win is not magic. It is less repeated explanation.
A good agent setup should feel boring in the best way. The agent reads the issue, checks the right files, pulls current docs, makes a small change, runs the test, and reports what happened. You still review the work, but you are no longer acting like a human clipboard.
The mistake is giving the agent every possible tool and hoping it figures life out. That just creates noise. Better setup:
- start with one repo
- add only the context sources that repo actually needs
- prefer read-only access first
- write rules for when each tool should be used
- keep memory small and durable
- verify outputs with tests, links, or logs
The best AI coding workflow in 2026 is not โprompt harder.โ
It is giving the agent a clean bench: current docs, scoped tools, project rules, memory that does not rot, and a verification loop.
Bigger prompts make agents look busy.
Better context makes them useful.
Top comments (9)
This is exactly where a lot of AI coding workflows get better: not by making the prompt larger, but by making the surrounding workflow smaller and clearer.
The best results I see usually come from giving the agent a narrow task, the right files, a concrete definition of done, and a fast verification loop. Giant prompts often hide missing structure instead of fixing it.
Context should be curated like an interface, not dumped like a folder.
Yep that's true ๐
Right. The funny part is that the better the model gets, the more obvious the workflow gaps become.
A strong coding agent still needs small tasks, clear repo conventions, and a way to verify the result. Otherwise it just produces a more confident version of the wrong change.
Ai is Generating valid code just the issue is CONTEXT. Ai companies should invest more on context management.
what say??
I agree. The tricky part is that context management is not just bigger context windows.
It is deciding what context gets loaded, what expires, what the agent is allowed to trust, and how the result gets verified.
For coding agents, I would rather see more investment in repo maps, task-scoped memory, retrieval tied back to actual files, and replayable tests/logs. More tokens help, but unmanaged context just gives the model a larger pile of noise.
Yep that's right but it makes ai more expensive...!
Nice talking to you ๐
Yep, that is the tradeoff. Better context is not free.
My guess is the pattern will be: do not use the biggest model and deepest context for every task. Spend the extra context only where failure is expensive โ production changes, migrations, security-sensitive work, anything hard to roll back.
For small tasks, cheap shallow context is fine. For serious code changes, managed context can be cheaper than debugging a confident wrong change later.
Nice talking to you too.
Totally agreed...!
Agreed โ "bigger prompts make agents look busy" is the right way to put it. I ran into a related problem where agents would skip the context-gathering phase entirely and jump straight to code changes. Put together Brainstorm-Mode (mehmetcanfarsak/Brainstorm-Mode on GitHub) which adds three modes (divergent, actionable, academic) via a PreToolUse hook to keep the agent in the right headspace for the phase. It pairs well with the "clean bench" approach โ forces the agent to actually read and understand before it starts touching files.