The AI Agent Document Myopia Trap: Why Static Documentation Blindfolds Autonomous Systems (And the 4-Plane Triangulation Fix)

// src/engine/EpistemicTriangulator.ts
import { Firestore } from "@google-cloud/firestore";
import { VertexAI } from "@google-cloud/vertexai";

export interface SignalPlane {
  livingStrategy: string;
  internalTelemetry: string;
  externalFrontier: string;
  staticPolicyDoc: string;
}

export interface TriangulatedResolution {
  governanceConstraints: string[];
  recommendedPayloads: string[];
  affirmativeActionPlan: string;
  confidenceScore: number;
}

export class EpistemicTriangulator {
  private db: Firestore;
  private vertex: VertexAI;

  constructor(projectId: string, location: string) {
    this.db = new Firestore({ projectId });
    this.vertex = new VertexAI({ project: projectId, location });
  }

  /**
   * Triangulates across all 4 operational planes to eliminate single-document myopia.
   */
  async triangulate(topic: string, rawDocText: string): Promise<TriangulatedResolution> {
    // Step 1: Concurrently gather context across living memory and real-time probes
    const [strategySnap, internalState, externalSignals] = await Promise.all([
      this.db.collection("agent_strategy").doc("active_pillars").get(),
      this.queryInternalTelemetry(topic),
      this.queryExternalFrontier(topic),
    ]);

    const livingStrategy = JSON.stringify(strategySnap.data() || {});

    // Step 2: Formulate prompt enforcing container-payload decoupling and affirmative invariants
    const model = this.vertex.getGenerativeModel({ model: "gemini-2.0-flash-001" });

    const prompt = `
You are an Epistemic Triangulation Engine. Analyze the following 4 signal planes for topic: "${topic}".

PLANE 1 (Living Strategy): ${livingStrategy}
PLANE 2 (1P Internal Telemetry): ${internalState}
PLANE 3 (3P External Frontier): ${externalSignals}
PLANE 4 (Static Policy Document): ${rawDocText}

INVARIANTS:
1. Treat Plane 4 as a historical baseline. Decouple immutable governance containers (security, auth, isolation) from transient illustrative payloads (model versions, old tool strings).
2. Cross-reference Plane 4 claims against Plane 2 (active reality) and Plane 3 (frontier state-of-the-art).
3. Formulate the output purely as Affirmative Operational Invariants (state what to execute, omitting negative prohibitions).

Output JSON with keys: governanceConstraints, recommendedPayloads, affirmativeActionPlan, confidenceScore.
`;

    const response = await model.generateContent({
      contents: [{ role: "user", parts: [{ text: prompt }] }],
      generationConfig: { responseMimeType: "application/json" },
    });

    return JSON.parse(response.response.candidates?.[0].content.parts[0].text || "{}");
  }

  private async queryInternalTelemetry(topic: string): Promise<string> {
    // Connect to live internal endpoint / search proxy
    return "Active 1P runtime endpoints: Verified healthy, Vertex AI Model Garden endpoints active.";
  }

  private async queryExternalFrontier(topic: string): Promise<string> {
    // Connect to external search proxy / Model Context Protocol benchmark catalog
    return "External ecosystem baseline: Terminal agents adopt MCP standards and dynamic model routing.";
  }
}