Builds

Flips traditional file based Git on its head for multi-agent engineering: the Abstract Syntax Tree (AST) graph is the primary source of truth, symbol-level leases prevent concurrent agent merge collisions, and files are materialized downstream for build tool compatibility.

from dataclasses import dataclass, field
from datetime import datetime, timedelta, timezone


@dataclass(frozen=True)
class ASTSymbolNode:
    symbol_id: str
    qualified_name: str
    kind: str  # "Function" | "Method" | "Struct" | "Interface"
    signature_hash: str
    dependencies: tuple[str, ...] = field(default_factory=tuple)


class CosmUniverseGraph:
    """Agent-first AST graph universe with symbol-scoped locking and blast-radius traversal."""

    def __init__(self, universe_name: str) -> None:
        self.universe_name = universe_name
        self.nodes: dict[str, ASTSymbolNode] = {}
        self.active_leases: dict[str, tuple[str, datetime]] = {}

    def register_symbol(self, node: ASTSymbolNode) -> None:
        self.nodes[node.symbol_id] = node

    def compute_blast_radius(self, target_symbol: str, max_hops: int = 2) -> list[str]:
        """Return all upstream symbols impacted if `target_symbol` mutates."""
        reverse_edges: dict[str, set[str]] = {sid: set() for sid in self.nodes}
        for sid, node in self.nodes.items():
            for dep in node.dependencies:
                reverse_edges.setdefault(dep, set()).add(sid)

        visited: list[str] = []
        frontier = {target_symbol}
        for _ in range(max_hops):
            next_hop: set[str] = set()
            for current in frontier:
                for caller in reverse_edges.get(current, set()):
                    if caller not in visited and caller != target_symbol:
                        visited.append(caller)
                        next_hop.add(caller)
            frontier = next_hop
        return visited

    def acquire_surgical_lease(self, symbol_id: str, agent_id: str, ttl_seconds: int = 30) -> dict[str, object]:
        now = datetime.now(timezone.utc)
        lease = self.active_leases.get(symbol_id)
        if lease and lease[1] > now and lease[0] != agent_id:
            raise RuntimeError(f"Symbol {symbol_id} locked by {lease[0]} until {lease[1].isoformat()}")
        expires = now + timedelta(seconds=ttl_seconds)
        self.active_leases[symbol_id] = (agent_id, expires)
        return {
            "universe": self.universe_name,
            "symbol_id": symbol_id,
            "agent_id": agent_id,
            "blast_radius": self.compute_blast_radius(symbol_id),
            "expires_at": expires.isoformat(),
        }

UNIVERSE: davenportjw/cosm-core (rev: 9f41c8a2)
LOCK STATUS: AVAILABLE (0 active agent leases)
SYMBOL: Router.Dispatch (kind: Method, lang: Go)
DIRECT CALLERS (depth=1): [Gateway.HandleRequest, ShardWorker.SyncTick]
TRANSITIVE IMPACT (depth=2): 4 symbols across 2 modules · Token savings vs full-file read: 78.4%

Routes agent prompts dynamically between Google Cloud Vertex AI (Gemini 3.8 Flash / Gemini 3.5 Flash-Lite) and local Gemma 4 runners based on complexity score, context token budget, and data residency policy.

from dataclasses import dataclass
from typing import Literal


@dataclass(frozen=True)
class RoutingPolicy:
    project_id: str = "davenport-boutique"
    location: str = "us-central1"
    frontier_model: str = "gemini-3.8-flash"
    fast_cloud_model: str = "gemini-3.5-flash-lite"
    local_edge_model: str = "gemma-4-9b-it"
    complexity_threshold: float = 0.65


def select_execution_route(
    prompt_tokens: int,
    requires_tool_calling: bool,
    complexity_score: float,
    policy: RoutingPolicy = RoutingPolicy(),
) -> dict[str, str | float | Literal["cloud-frontier", "cloud-fast", "edge-gemma"]]:
    """Select optimal Gemini 3.8+ or Gemma 4 target using explicit SLO rules."""
    if requires_tool_calling or complexity_score >= policy.complexity_threshold or prompt_tokens > 8192:
        return {
            "tier": "cloud-frontier",
            "model": policy.frontier_model,
            "project": policy.project_id,
            "location": policy.location,
            "auth": "gcloud-adc",
            "rationale": f"High complexity ({complexity_score:.2f}) or tool synthesis required.",
        }
    if prompt_tokens > 2048:
        return {
            "tier": "cloud-fast",
            "model": policy.fast_cloud_model,
            "project": policy.project_id,
            "location": policy.location,
            "auth": "gcloud-adc",
            "rationale": "Medium context window routed to Gemini 3.5 Flash-Lite.",
        }
    return {
        "tier": "edge-gemma",
        "model": policy.local_edge_model,
        "project": policy.project_id,
        "location": "local-runner",
        "auth": "local-ipc",
        "rationale": "Compact classification task eligible for Gemma 4 runner.",
    }

ROUTE DECISION: vertex-ai/gemini-3.8-flash (project=davenport-boutique, location=us-central1)
REASON: multi-symbol code mutation (complexity=0.82 > threshold=0.65)
AUTH MODE: google-cloud-adc (zero API key) · EST. LATENCY: 410 ms

Inspired by sleep-phase memory consolidation: episodic agent session trajectories are clustered offline during 'dream cycles' and promoted into high-confidence crystallized developer beliefs in Google Cloud Spanner.

from dataclasses import dataclass


@dataclass(frozen=True)
class EpisodicObservation:
    session_id: str
    rule_candidate: str
    success_rate: float


@dataclass(frozen=True)
class CrystallizedBelief:
    belief_id: str
    statement: str
    confidence: float
    supporting_sessions: int
    scope: str = "global"


def consolidate_dream_cycle(
    observations: list[EpisodicObservation],
    min_sessions: int = 3,
    min_confidence: float = 0.90,
) -> list[CrystallizedBelief]:
    """Promote recurring episodic observations into crystallized Spanner beliefs."""
    grouped: dict[str, list[EpisodicObservation]] = {}
    for obs in observations:
        grouped.setdefault(obs.rule_candidate.strip(), []).append(obs)

    promoted: list[CrystallizedBelief] = []
    for idx, (statement, items) in enumerate(sorted(grouped.items()), start=1):
        unique_sessions = {i.session_id for i in items}
        if len(unique_sessions) < min_sessions:
            continue
        avg_score = sum(i.success_rate for i in items) / len(items)
        confidence = round(min(0.99, avg_score * (1.0 + 0.02 * len(unique_sessions))), 2)
        if confidence >= min_confidence:
            promoted.append(
                CrystallizedBelief(
                    belief_id=f"BELIEF-{idx:03d}",
                    statement=statement,
                    confidence=confidence,
                    supporting_sessions=len(unique_sessions),
                )
            )
    return promoted

DREAM CONSOLIDATION COMPLETE · Scanned 18 session trajectories
PROMOTED BELIEF #042: 'Always run uv run pytest -v and maintain <3s unit test execution' (confidence=0.97)
SPANNER COMMIT: projects/davenport-boutique/instances/electric-sheep/databases/memory

Bridges the gap between ephemeral LLM reasoning traces and permanent repository commit history by binding OpenTelemetry span IDs, tool-call trajectories, and token budgets directly to AST/Git mutations.

from dataclasses import dataclass
import hashlib


@dataclass(frozen=True)
class TrajectoryStepSpan:
    step_index: int
    tool_name: str
    duration_ms: float
    input_tokens: int
    output_tokens: int
    status: str = "OK"


def build_commit_lineage_manifest(
    commit_sha: str,
    model_name: str,
    spans: list[TrajectoryStepSpan],
) -> dict[str, object]:
    """Bind an agent trajectory span sequence to a deterministic lineage digest."""
    total_ms = round(sum(s.duration_ms for s in spans), 2)
    total_tokens = sum(s.input_tokens + s.output_tokens for s in spans)
    fingerprint_payload = f"{commit_sha}:{model_name}:{len(spans)}:{total_tokens}"
    lineage_id = hashlib.sha256(fingerprint_payload.encode("utf-8")).hexdigest()[:16]
    return {
        "lineage_id": f"lin_{lineage_id}",
        "commit_sha": commit_sha,
        "model": model_name,
        "step_count": len(spans),
        "total_duration_ms": total_ms,
        "total_tokens": total_tokens,
        "tools_invoked": [s.tool_name for s in spans],
    }

LINEAGE RECORD: commit=9f41c8a2 -> trace_id=4bf92f3577b34da6a3ce929d0e0e4736
MODEL: gemini-3.8-flash · STEPS: 6 · TOOL CALLS: 4 (read_file, edit_file, pytest)
VERIFICATION GATE: PASSED (100% pass rate in 1.42s)

Regional Cloud Run simulation shards (`den-1`, `slc-2`, `phx-3`, `abq-4`) running an authoritative 10 Hz isometric forklift physics loop with `#dash` battery boost mechanics and AI bot fleet dispatch.

from dataclasses import dataclass


@dataclass
class ForkliftState:
    unit_id: str
    x: float
    y: float
    battery_pct: float = 100.0
    carrying_pallet: bool = False
    pallets_delivered: int = 0


def step_forklift_physics(
    state: ForkliftState,
    dx: float,
    dy: float,
    boost_active: bool,
    dt_sec: float = 0.1,
) -> ForkliftState:
    """Authoritative 10 Hz forklift movement & #dash battery consumption step."""
    can_boost = boost_active and state.battery_pct >= 8.0
    speed_multiplier = 1.85 if can_boost else 1.0
    base_speed = 4.2 if not state.carrying_pallet else 3.4

    new_x = max(0.0, min(24.0, round(state.x + dx * base_speed * speed_multiplier * dt_sec, 3)))
    new_y = max(0.0, min(16.0, round(state.y + dy * base_speed * speed_multiplier * dt_sec, 3)))

    if can_boost and (dx != 0 or dy != 0):
        battery = max(0.0, round(state.battery_pct - 14.0 * dt_sec, 1))
    else:
        battery = min(100.0, round(state.battery_pct + 4.5 * dt_sec, 1))

    return ForkliftState(
        unit_id=state.unit_id,
        x=new_x,
        y=new_y,
        battery_pct=battery,
        carrying_pallet=state.carrying_pallet,
        pallets_delivered=state.pallets_delivered,
    )

HTTP/2 200
content-type: text/html; charset=utf-8
x-frame-options: DENY
server: Google Frontend

Browser-native multi-turn docent architecture running on-device via Chrome's LanguageModel and Summarizer APIs. Uses document.modelContext (WebMCP) to expose grounded client-side tools and falls back dynamically to Google Cloud Vertex AI (gemini-3.8-flash) via Application Default Credentials.

// 1. Probe & Warm Base Session via Chrome Prompt API
const capabilities = await self.LanguageModel?.availability();
const baseSession = await self.LanguageModel.create({
  initialPrompts: [{ role: "system", content: "You are The Davenporter Docent..." }]
});

// 2. Register Client-Side WebMCP Tools into document.modelContext
if ("modelContext" in document && typeof document.modelContext.registerTool === "function") {
  document.modelContext.registerTool({
    name: "search_davenporter_records",
    description: "Search essays, blueprints, and simulations",
    parameters: {
      type: "object",
      properties: { query: { type: "string" } },
      required: ["query"]
    },
    execute: async ({ query }) => searchCatalogRecords(query)
  });
}

// 3. O(1) Session Cloning for Turn Isolation & Safe Streaming
const turnSession = await baseSession.clone();
const stream = turnSession.promptStreaming("Explain cosm AST blast radius");
for await (const chunk of stream) {
  messageBodyElement.append(chunk); // textContent append only - zero innerHTML
}

// 4. Chrome Built-in Summarizer API
const summarizer = await self.Summarizer.create({ type: "key-points", length: "medium" });
const summaryStream = summarizer.summarizeStreaming(document.body.innerText);

ENGINE: vertex-fallback (gemini-3.8-flash) | LATENCY: 284ms
MODEL_CONTEXT: document.modelContext grounded 3 tools [search_davenporter_records, inspect_davenporter_record, summarize_active_record]
AUTH: Google Cloud ADC (davenport-boutique) | STATUS: 200 OK
RECORDS_CITED: ['cosm', 'smartrouter']