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# Agent workflow & collaboration — guide (loop tier)

Reference notes, design rationale, and negative results (13).

#### 1. Design-first: write strategy/design docs before code, and never trust a 'no open questions' doc
`loop` · ⚠ _session-derived, unverified_

For a new player or substantial behavior, write per-role {ROLE}_STRATEGY.md from the rulebook/game API plus a tying DESIGN.md BEFORE coding decision logic -- forcing strategy into prose surfaces win-condition structure and asymmetries that otherwise leak in as ad-hoc code. A design doc claiming 'no open questions' is a smell: real player designs still have unresolved API, timing, identity, pathfinding, testing, and mode-composition questions. Treat 'implementation-ready' as a bar (every mode has params + behavior spec + exit conditions, every decision is a tree/table, error/recovery paths enumerated) and run the doc through an explicit issue-by-issue review pass, auditing it like code, before applying fixes.
  <sub>sources: codex:019dfe49-9c5e-76c0-86b8-99fbc2cf01b3, opencode:ses_1fbfc5c8fffesCp5Ilc2Hs3Q6X, opencode:ses_1fb8906dcffete6Fy63Vc7IEPv, opencode:ses_209613a85ffeqO1Gq9NEDobBYi</sub>

#### 2. Prefer agent-driven generation, reserve Python for glue, and build iteration tooling on demand
`loop` · **negative result** · tool: `maker_v2`

In a code-generation toolkit, prefer driving a coding-agent runner with a focused prompt plus a small validation step over hand-coded Python extraction or per-game heuristics; reserve deterministic Python for glue, schema enforcement, manifest emission, Dockerfile assembly, packaging, and tests -- not for parsing prose. A contract-first, composable, agent-driven pipeline (maker_v2) deprecated the hand-coded-deriver (maker_v1); point agent-guide generation prompts at the SAME in-repo framework (e.g. players.player_sdk) the downstream agent-maker will actually use. When a deprecated stage must serve as a temporary fallback because its successor lacks a capability, file that gap as a requirement AGAINST the successor rather than adding new features to the deprecated stage, so the deprecated path does not accrete work. Favor human-in-the-loop pipelines that turn a source tree into developer artifacts over end-to-end auto-coding, and build iteration tooling on demand as experience reveals what is needed, not up front.
  <sub>sources: players_checkouts/players/tools/cogbase/docs/designs/maker_v2_design.m, players_checkouts/players/tools/cogbase/README.md, players_checkouts/players/tools/cogbase/docs/artifact_pipeline.md, opencode:ses_223dc7136ffeCr4ZslnR426sVG</sub>

#### 3. Nightly champion loop: gate AUC>=0.70 / >=500 games, and know its silent-failure modes
`loop`

An automated nightly champion loop can run scrape -> refit -> gates (AUC>=0.70, >=500 games, full test suite, in-image smoke gate) -> vendor -> build -> upload -> submit on a cron schedule. Beware its failure modes: it silently no-ops if the machine sleeps through the scheduled time, and aborts if cloud auth expires or Docker is down. A full-corpus fit ignores recency, so add a recency window if field drift outpaces accumulation.
  <sub>sources: personal_labs/crewrift_lab/WORKING_CONTEXT.md</sub>

#### 4. When independent players converge on the same shape, extract a shared framework grounded in interface contracts
`loop` · ⚠ _session-derived, unverified_

When multiple independently-built coworld players converge on the same shape, extract a shared framework rather than copy-pasting: three players (escape-room, social-deduction, resource-competition) all shared an inner perception->belief-update->decision->action loop driven by a symbolic mode-level policy plus an outer strategy loop that selects the active mode (possibly an LLM) -- that convergence is the signal worth abstracting. Expose the game-specific variable parts as interfaces (perception/frame-parsing, belief schema, the set of modes, the strategy/mode-selector) and let the framework own the fixed parts (runtime loop, mode controller, belief buffers, tracing). When shipping such a framework: ship a toy non-game example exercising the interfaces as a copyable template; leave a SOURCE_REPOS pointer file recording source repo paths; strip original internal player names from the user-facing README (keep provenance in a separate reference doc); ground the generalization in the source-code contracts (mode interfaces, belief-snapshot structs, directive schemas, buffer/channel boundaries) NOT the READMEs; and preserve any more-complete interactive chat API rundown verbatim in a 'Framework Reference' section. When one player has already solved a cross-cutting problem (e.g. a process-wide counter plus per-instance trace-dir mechanism), inspect the solved player first and port its pattern rather than reinventing it.
  <sub>sources: codex:019e18b2-4478-78d0-8882-752b7b0d55c9, codex:019e18d7-532e-7540-82ea-9abe1726aec7, codex:019e1914-2445-7fa2-af5e-8d3ca4d01c57, opencode:ses_2044035bcffeRp9M35307uA4gR (+2)</sub>

#### 5. Split the agent into a fast symbolic inner loop and a slow LLM outer loop emitting durable intents; gate stale directives
`loop` · ⚠ _session-derived, unverified_

Split an agent into a fast per-tick symbolic inner loop and an async LLM outer loop, where the outer loop only selects the active 'mode' (durable strategic intent) and the inner loop reactively selects tactical tasks each tick. Design outer-loop decisions to age gracefully: an LLM call taking 1-2s means its belief snapshot may be 24-48+ ticks stale by the time the directive lands, so the outer loop emits DURABLE intents and the inner loop handles tactical correction reactively. Do not apply stale outer-loop directives unconditionally -- gate them on tick/phase/view preconditions and add staleness/rejection rules, because in short rounds (e.g. 15s) a 1-2s latency is strategically large and an old-state directive can be actively harmful. Do not throttle the outer loop with per-tick sleeps (a 50ms sleep was removed) and keep an explicit mode directive crossing the outer-to-inner boundary rather than having the inner loop infer mode from shared memory. Make tasks coarse-grained multi-frame behaviors so the LLM never reasons about frame-level input: each task (explore, move_to, pursue_player, open_chatroom, offer/accept_role_exchange, shout) encapsulates a behavior with an act(perception, belief) -> FrameAction method and owns its button sequencing -- this task seam is what makes the latency decoupling work. Allow a deliberate, documented exception where a task must block the fast loop (a chat_and_observe task makes synchronous LLM calls to compose chat text, intentionally blocking because the agent cannot meaningfully act while writing).
  <sub>sources: claude-code:27984094-3b9e-4e67-92c5-da2a61ceb32a, claude-code:fa645b7b-4fa0-4b55-992e-b273ee703391, codex:019dfe49-9c5e-76c0-86b8-99fbc2cf01b3, codex:019e1914-2445-7fa2-af5e-8d3ca4d01c57 (+1)</sub>

#### 6. Isolate the LLM outer loop behind buffers, validation, snapshots, and a no-op default
`loop`

Connect an async LLM outer loop to the inner loop via two size-1 consume-on-read (latest-wins) buffers so the inner loop never blocks; if the outer loop is slow, crashes, or absent, the inner loop keeps running and a watchdog activates an agent-defined fallback mode after ticks-since-last-directive exceeds a threshold (default 120 ticks ~= 5s), restarting the thread on crash. Restrict the outer loop's only write channel to a dedicated 'inferences' namespace replaced wholesale each cycle, keep framework-managed fields read-only to it, and validate mode directives at CONSUMPTION time in the inner loop (mode in registry, params isinstance the declared type) -- on invalid, log and keep the current mode. Snapshot (deep-copy) belief state before each agent hook and roll back if it raises, then continue with the next hook (hooks must not assume prior hooks succeeded). Gate all LLM runtime control behind a deterministic validation layer with a closed semantic decision schema, a JSON-safe context, an offline shadow runner, and a no-op 'hold' provider as default, so the agent is fully runnable and testable without any model calls.
  <sub>sources: orpheus/DESIGN.md (Orpheus agent framework design), players_checkouts/players/users/james/personal_cogs/persephone/README.</sub>

#### 7. Orpheus inner loop: phase-agnostic, perception-inferred completion, view-gated tasks
`loop`

Run the per-tick pipeline (perception, belief-update, decide, act) in the same order every tick regardless of game phase, with no framework-level phase gating -- phase-appropriate behavior emerges from modes returning idle/no-op tasks, because sending an empty button mask in a non-interactive phase is harmless. Give every task a valid_views set; before calling a task the framework checks belief_state.view against it and emits a no-op on mismatch. Do not let tasks self-report completion: a task can only assert it sent a command, not that the world changed -- treat completion as a belief-level concept inferred from perception, and if an action's effect is undetectable, fix the perception gap rather than adding a completion-signal channel. Separate per-task execution state (ActionMemory: path waypoints, commands sent, ticks active) from durable belief state and clear it only when task identity (type + params) changes, so re-affirming the same task preserves in-progress execution.
  <sub>sources: orpheus/DESIGN.md (Orpheus agent framework design)</sub>

#### 8. Build a per-tick game policy as a crash-safe four-stage deterministic pipeline
`loop`

Structure a scripted per-agent policy as perceive (parse sparse observation tokens into a delta, write seen cells to sparse spatial memory) -> interpret (run to fixpoint deriving higher-order beliefs) -> decide (select exactly one validated skill) -> handle_failure (emit a safe no-op if any stage raised). Wrap EACH stage in its own try/except (not one big one) so an exception never kills the episode and the trace gets per-stage failure categories (PERCEIVE_RAISED, INTERPRET_RAISED, DECIDE_RAISED, SKILL_NOT_FOUND, PRECONDITION_FALSE, ARG_VALIDATION_FAILED, SKILL_RAISED, SKILL_RETURNED_INVALID) telling an offline optimizer exactly where to look. The handler is itself improvable, but wrap it in one final try/except emitting a hardcoded Action(name='noop') with no Python in the loop -- this last-resort fallback is structurally guaranteed not to fail and is intentionally NOT improvable. Iterate Interpret to a fixpoint by hashing canonical-form JSON working memory before/after each pass, stopping on a match or at MAX_INTERPRET_ITERS (cap 8, hitting it is a defect signal to log); never retry a failed stage within the same tick because rerunning corrupted state compounds the failure.
  <sub>sources: archive/cogames_playground/alpha_cog/docs/agent-architecture.md, archive/cogames_playground/alpha_cog/docs/paper/paper.md, archive/cogames_playground/alpha_cog/docs/cogs-v-clips-strategy.md</sub>

#### 9. Measure per-tick latency don't enforce it; cap working memory; keep skills reactive
`loop`

Do not use hard timeouts to enforce a per-tick latency budget in a synchronous Python policy: OS signals are unix-only and not thread-safe, and subprocesses are too heavy for a ~10ms budget. Measure wall time per stage with time.perf_counter() and treat budget violations as an offline optimizer signal, not a runtime intervention. Cap serialized JSON working memory (e.g. 128 KB) and log loudly on violation -- the cap exists to surface bugs (an interpret rule appending every pass), not save storage. Forbid skills from calling each other directly; encode composition as a sequence/intent in working memory and re-decide each tick, keeping the agent reactive over a short horizon (matching partial observability). Size skills at role-logic granularity (move_to_coord, pick_up_nearest_heart, flee_from_threat) -- smaller adds decision overhead, larger is hard for an LLM to rewrite -- and expect to refactor the first pass at least once, so start small.
  <sub>sources: archive/cogames_playground/alpha_cog/docs/agent-architecture.md</sub>

#### 10. Use ring buffers for exact windowed queries, EMAs only for approximate trends
`loop`

For temporal reasoning in a per-tick policy, use a hybrid memory layer: ring buffers where you need precise windowed queries (e.g. minimum distance-to-target over the last N ticks for stuck detection) and exponential moving averages where approximate trends suffice (deficit fraction, deposit-throughput rate). EMAs are O(1) memory per signal but lack precise window semantics, so do not use them where you need an exact 'fraction of last N ticks' answer.
  <sub>sources: archive/cogames_playground/alpha_cog/docs/temporal-memory-design.md</sub>

#### 11. Coordinate multi-agent teams deterministically through in-game channels only
`loop`

Enforce strict per-agent isolation: each agent gets its own working/spatial memory with no shared blackboard or out-of-band channel, so coordination flows only through in-game communication actions available in tournament play -- avoiding private infrastructure you must rip out before submission. Derive each agent's opening role/resource/direction deterministically from its own slot id (visible to nearby teammates) so every agent independently reaches the same assignment with no contention, and teammates running other policies cannot break it. With two channels, use the persistent one-byte 'vibe'/role signal as the cheap identity channel emitted once per role transition, and reserve the text/talk channel for news the receiver cannot infer (resource sightings, extractor depletion, death locations).
  <sub>sources: archive/cogames_playground/alpha_cog/docs/agent-architecture.md, archive/cogames_playground/bulbacog/designs/STRATEGY_V2.md</sub>

#### 12. Build screen-reading game players in capability order: see before you strategize
`loop`

Build a screen-reading player perception-first, in capability order: read the URL and show frames, unpack frames, send/verify input masks, add a debug viewer, decide what geometry to vendor, package linux/amd64, THEN add interstitial detection, localization with dynamic-pixel masking, temporal+spiral search, task-icon detection, radar, momentum-aware A*, task hold, body reporting, voting, imposter and ghost behavior, and only then upload and profile. Structure per-frame logic as a linear pipeline (connect, receive, unpack, tick, detect interstitial, parse/update state, localize camera, update tasks, choose goal, choose input mask, send, draw debug) rather than callbacks, because a pipeline is far easier to debug. Do not start with clever strategy; start with seeing, localizing, and drawing what the bot believes.
  <sub>sources: bitworld/among_them/players/how_to_make_a_bot.md</sub>

#### 13. Stage agent-framework builds bottom-up; auto-discover agents by convention
`loop`

Stage agent-framework implementation so each layer builds on a tested lower one: core data types/interfaces first, then a no-op end-to-end pipeline skeleton, then belief update, spatial/pathfinding, tasks, hooks, mode switching, outer loop, logging, and finally a test agent for integration. Auto-discover agents by convention (agents/*/policy.py) with a simple per-agent contract: runnable as a script accepting --url and --name, plays until disconnected, exits cleanly on SIGINT/SIGTERM, and exposes optional module-level metadata constants. Structure a risky multi-phase build as logically mergeable phases (e.g. scaffold, perception port, crewmate, meetings/voting, imposter) with a stop-and-review and explicit done-when criteria at each boundary, broken into Graphite stack sub-commits ordered by dependency. Keep a deliberately minimal 'tiny baseline' policy (single-file ~250-line DemoPolicy: mine one heart-recipe resource, deposit, wander from walls when idle; no roles/gear/coordination) as a readable reference newcomers read first.
  <sub>sources: orpheus/IMPLEMENTATION_PLAN.md (Orpheus implementation plan), players_checkouts/players/users/james/personal_cogs/persephone/README., players_checkouts/players/archive/players/among_them/coborg/PLAN.md, players_checkouts/players/players/cogsguard/tiny_baseline/README.md</sub>
