1What happens to price discovery when a growing share of the traders in a market are autonomous language-model agents? We build an event-sourced continuous double-auction exchange with exact accounting, a latent dividend process that separates fundamental value from public value and transaction price, seven calibrated classical trader types, and an adapter that lets a language model trade from a canonical snapshot through a strictly validated JSON action. We then run randomized, matched-seed experiments in which 0 to 100 percent of trader slots are exogenously assigned to a small reasoning model (gpt-5.4-nano), holding fundamentals, news, endowments, latencies and information sets fixed, with a pre-registered analysis plan, seven matched seeds per live cell and thirty per classical cell. A stylized model with correlated signal errors predicts that a shared model degrades informativeness, raises order-flow correlation and amplifies leverage-driven spirals, and that model diversity restores informativeness. The simulator confirms the predictions on coordination and diversity and rejects them on prices: raising the LLM share tightens quoted spreads from 19 to 6 basis points, multiplies depth by seven, cuts price impact by 60 percent and leaves the pricing error unchanged, while the market absorbs latent information more slowly and returns become predictable (variance ratio 2.1, lag-one autocorrelation 0.32 at full penetration). No bubble or crash appears in 112 live runs, and with leveraged traders present the LLM agents halve the pricing error and remove the bubble episodes the leverage creates. Prompt diversity and a two-model mix lower coordination and shorten absorption at a liquidity cost; a communication channel and memory raise coordination and pricing error slightly. The unifying mechanism is patient, public-value-anchored liquidity provision by agents who act infrequently. All results are causal statements about the simulator, reproducible from a one-command replication package; the Anthropic cells of the design are implemented but could not be run.2