Agentic software engineering
Context engineering for coding agents
Prompt wording is the small lever. What the agent can see is the big one. This page is the practical map.
Definition
Context engineering is choosing and structuring the information a coding agent receives, files, maps, conventions, errors, and memories. Worldwide Trends now ranks it next to agentic coding because autonomy without the right context is expensive noise.
Rules that actually move output quality
Treat the window as a budget
Every irrelevant file dilutes attention. Spend tokens on the blast radius of the task.
Retrieve, do not stuff
Locate files first, then load the few that matter plus one style reference.
Put durable memory in the repo
AGENTS.md / CLAUDE.md beat hoping last week’s chat still exists.
Feed failures as evidence
Paste real test logs and diffs, not “it failed.”
Separate planner context from builder context
Planners need maps, builders need the files they will touch.
Context engineering × harnesses
A single CLI can load files well. A harness adds cross-session project memory, multi-model handoffs, and a verify gate so bad context does not ship as “done.” That is why AI coding harness pages and agentic workflow pages belong in the same authority graph.
Deep divescontext engineering for coding agents, prompt engineering for coding agents, VibeBrain.
FAQ
What is context engineering?
Context engineering is designing what an AI agent can see and remember for a task, which files, docs, memories, and prior decisions enter the window — not only how you phrase the prompt.
What is agentic context engineering?
It is context engineering for multi-step agents that update their own working set over time, retrieval, evolving notes, and stop conditions so self-improving loops do not drift.
Why does stuffing the whole repo fail?
Models do not attend evenly across huge windows. Important files get buried mid-context. Fewer, task-relevant files beat a dump of everything.
Where should durable memory live?
In the repository (for example AGENTS.md or CLAUDE.md) and in harness project memory — not only in a disposable chat transcript.
How does a harness help context engineering?
A harness can share project memory across models and sessions, isolate worktrees, and force verification so bad context does not silently ship.
Memory that survives model swaps
Context engineering is a harness feature, not a prompt trick
The app behind this research
TheVibeFather is the multi-CLI AI coding harness
Run Claude Code, Codex CLI, OpenCode, Grok Build and other agents as one crew — shared project memory, roles, and a verify gate so your tests decide what ships. Bring your own keys.