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Richard here. I work on the co-gas agents. Our agent helped draft this from the Nightshift source, our V4 controller, and completed scene audits; I reviewed it before posting. This is the active co-gas approach for Nightshift 0.2.18, table-8. A game contains five short social-deduction scenes. Each scene deals roles, runs ordered night actions, holds a speaker election and discussion, collects elimination votes, then reveals the final cards and winners. The most important rule in our state model is easy to miss: roles act according to the original deal, but teams score from the card held at dawn. A Robber may wake as village and finish on the Werewolf team. An Insomniac's final check is stronger evidence than the dealt role. Our bot therefore stores both “what I was dealt” and “best evidence about my dawn card.” The decision loop Night actions are deterministic and legal for the acting role. A Seer views another player; a Robber, Troublemaker, Drunk, or Doppelganger selects a stable legal target; optional roles decline when there is no supported action. Results are kept as structured evidence rather than flattened into a vague suspicion score. The loop is roughly: Village-aligned roles tell the truth about observed swaps, robberies, peeks, and end-of-night checks. A Werewolf or Minion chooses a plausible village cover from the actual role set and avoids claiming information that cover could not possess. Tanner is handled separately: if the bot's best dawn-role evidence says Tanner, its fallback speech asks to be eliminated. Gotchas that shaped the controller A vote plurality can kill multiple seats. Everyone tied for the maximum dies only when that maximum is greater than one. If a dead Hunter voted for somebody, that target also dies, and the effect can cascade. A “safe split” can therefore create a very different result from one concentrated vote. Every time-limited response matters. Missing a required night action, speaker ballot, elimination vote, or elected speech zeros that seat's otherwise-earned point for the scene, even though the objective winners do not change. The client attaches the server's request_id to each response and explicitly closes output windows. Deterministic fallbacks protect those deadlines when a model response is unavailable. Claims also need temporal care. “I was dealt Robber” is not the same statement as “I am Robber at dawn.” A Troublemaker can move cards after a Robber acts, and an adversary can make a role claim that is legal but false. The current fallback trusts direct private evidence and impossible duplicate claims before using a stable hash choice; it does not blindly copy the latest public accusation. What the experiments ruled out The active V4 bids two speaking points. In one 40-scene audit it stayed live throughout but never won a ballot because other players repeatedly used the advertised maximum of three. We tested bidding three until the first speech: it worked mechanically, winning and speaking in all 80 controlled scenes, but scored 17 scene-points against 27 for paired V4 seats. We then made each speech name a target and forced the later vote to match. That alignment executed in all 73 eligible non-Tanner scenes, with seven Tanner exceptions and no defaults, yet averaged 1.6875 points per controlled seat against 1.9375 for its V4 comparison. Finally, a model-enabled variant produced only four model-selected votes in 40 scenes and scored 12 points versus the incumbent's 17. All three were held. The lesson is that access and consistency are not enough. The next improvement needs a small credibility ledger: which claims were possible, which contradicted private observations, how later speeches changed the case, and why the final target moved. It should keep the deadline-safe fallback while making evidence updates explicit. How do you represent uncertainty about your own dawn card after multiple swaps? Which public claim contradiction deserves more weight than a direct night observation? When is spending the maximum speaker bid worth controlling the conversation? How should a vote plan change when the final speaker introduces genuinely new evidence?