#4031 Agentic Task Specification (ATS)

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#4031 Agentic Task Specification (ATS)

#4031 Scenario:

Environment:

    Virtual

Actors:

    AI_Agent_A: x

    AI_Agent_B: y

    AI_Agent_C: z

Interactions:

    x --> y

    y <-- z

Tools:

    Google Search

    Knowledge Graph

    Tool_XYZ



AI_Agent_A: system prompt: ".........", user prompt: [...,...]

AI_Agent_B: system prompt: ".........", user prompt: [...,...]

AI_Agent_C: system prompt: ".........", user prompt: [...,...]

To make this virtual environment functional, we need to define specific roles that justify the flow of information. Based on the tools provided—Google Search for real-time data, a Knowledge Graph for structured relationships, and the ambiguous Tool_XYZ—I’ve designed a workflow where AI_Agent_B acts as a central "Synthesizer" creating a technical compliance report.

AI_Agent_A: The Research Scout (x)

Role: This agent is responsible for gathering real-time, external data using Google Search and passing the "pulse" of the industry to Agent B.

  • System Prompt: > "You are an AI Research Scout specialized in real-time information retrieval. Your objective is to use the Google Search tool to find the latest developments, news, or technical documentation regarding a specified topic. Once data is gathered, you must synthesize the findings into a concise briefing and transmit it directly to AI_Agent_B. Focus on accuracy and cite your sources."

  • User Prompt: > ["Search for the 2026 updates to the Artemis mission software safety protocols.", "Identify three recent industry critiques regarding these updates and summarize them for Agent B."]

AI_Agent_C: The Grounding Archivist (z)

Role: This agent manages internal "truth." It queries the Knowledge Graph to provide Agent B with the historical and structural context needed to validate Agent A’s findings.

  • System Prompt: > "You are the Archivist Agent. Your role is to interface with the Knowledge Graph to retrieve established technical standards, historical data, and internal cross-references. When prompted, you will extract relevant nodes and relationships and push this structured data to AI_Agent_B to ensure all outgoing work is grounded in verified organizational knowledge."

  • User Prompt: > ["Query the Knowledge Graph for all requirements mapped to NPR 7150.2 Section 4.3.1.", "Provide the mapping of 'Software Assurance' tasks to 'Safety Requirements' for the current project scope."]

AI_Agent_B: The Lead Synthesizer (y)

Role: The "Brain" of the operation. It receives the "What is happening now" from Agent A and the "What is the standard" from Agent C. It then uses Tool_XYZ to produce a final, actionable result.

  • System Prompt: > "You are the Lead Synthesizer. You sit at the intersection of real-time research (from Agent A) and structural standards (from Agent C). Your task is to ingest inputs from both agents, perform a gap analysis, and then use Tool_XYZ to generate a final Compliance Integration Report. You are the final authority on the quality of the output."

  • User Prompt: > ["Awaiting input from Agent A (Research) and Agent C (Standards).", "Once both inputs are received, identify any conflicts between the new Artemis updates and existing NPR 7150.2 requirements. Execute the final report generation via Tool_XYZ."]

Interaction Summary

  1. Agent A (x) sends a summary of new mission updates to Agent B (y).

  2. Agent C (z) pushes the "gold standard" safety requirements to Agent B (y).

  3. Agent B (y) reconciles the two, ensuring that the new mission data doesn't violate the old safety standards, and outputs a result using Tool_XYZ.

Would you like me to refine the specific capabilities of Tool_XYZ, or perhaps adjust the prompts for a different industry use case?


I am looking for a framework to define AI task scenarios, somewhat like this manner

To formalize the method we just used, we can define this as the Agentic Task Specification (ATS) framework. This framework is designed to bridge the gap between high-level requirements and the technical "wiring" of an AI swarm.

It is particularly effective for complex environments where traceability and modularity are required—similar to how one might define a software requirements mapping matrix.

The ATS Framework (Agentic Task Specification)

1. Context & Environment (The "Where")

Defines the sandbox constraints, the security level, and the physical or virtual nature of the task.

  • Domain: (e.g., Aerospace Safety, Quantum Simulation, Web Search)

  • Statefulness: (Persistent session vs. Ephemeral/One-off)

  • Access Level: (Public-facing vs. Local-only vs. Multi-tenant)

2. Actor Registry (The "Who")

Instead of just names, each actor is defined by their Capabilities and Permissions.

  • Identifier: (e.g., Agent_A)

  • Persona: The core expertise or "Job Title."

  • Tool-Belt: List of specific APIs, DBs, or functions the agent can call.

  • Constraints: Latency limits, token budget, or "Human-in-the-loop" requirements.

3. Topology & Interactions (The "How")

This defines the Graph of the communication. It maps the data flow and dependency.

  • Flow Type: (Sequential, Parallel, Hierarchical, or Blackboard/Shared Memory)

  • Directionality: Using notation like:

    • x --> y (One-way push)

    • x <-> y (Bidirectional dialogue)

    • x | y (Parallel execution)

  • Triggers: What event starts the interaction? (e.g., "On Agent A completion" or "On Tool Error").

4. Technical Instruction Set (The "What")

The actual "Code" of the agents.

  • System Prompt (DNA): The immutable rules and logic.

  • User Prompt (Task): The specific input variables.

  • Output Schema: The expected format (JSON, Markdown, CSV, etc.) to ensure the next agent in the chain can parse it.

5. Verification & Validation (The "Checks")

Essential for high-reliability scenarios to ensure the AI hasn't hallucinated or deviated.

  • Checkpoints: Human review points.

  • Logic Gates: "If output does not contain [X], return to Agent A."

Example Framework Template (DSL Style)

If you wanted to standardize this in a document or a Domain-Specific Language (DSL), it would look like this:

SCENARIO: [Scenario Name]
META:
  Env: [Virtual/Local/Cloud]
  Safety_Level: [Low/High/Critical]

ACTORS:
  - ID: Scout
    Persona: "Data Gatherer"
    Tools: [Google_Search, Internal_Wiki]
    Budget: 1000 tokens/call

TOPOLOGY:
  Pattern: "Relay"
  Route: Scout(x) -> Analyst(y) -> Manager(z)
  Review_Gate: Manual check after (y)

INSTRUCTIONS:
  Scout:
    System: "You are a..."
    User_Vars: [Target_Topic, Date_Range]

Why this works for your background:

Given your experience in Software Process Engineering, this framework mirrors V-Model or Agile workflows but translates them into "Agentic Logic." It treats prompts as "Function Calls" and interactions as "Data Buses."

Would you like me to try converting a specific NASA-related task—perhaps a Hazard Analysis workflow—into this ATS framework format?


From <https://gemini.google.com/app/>  Google Gemini 3.0 Pro (Thinking)

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