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Frameworks for AI Adoption: The RIP Test

Most AI initiatives fail not due to model capability, but due to poor target selection. Companies attempt to replace "roles" (which are ambiguous clusters of responsibilities) rather than "tasks" (which are discrete units of work).

To determine if a workflow is suitable for autonomous agent execution, we can apply a heuristic filter: The RIP Test.

The Heuristic

For a task to be effectively offloaded to an LLM-based agent with current generation models, it must score highly on three axes:

  1. Repetitive
  2. Isolated
  3. Predictable

Axis 1: Repetitive (Frequency)

High-frequency tasks justify the "Context Engineering" overhead. If a task is performed once a month, the cost of writing the prompt and testing the agent exceeds the time saved.

  • Positive Signal: "Process inbound invoice" (100x/day).
  • Negative Signal: "Write the annual strategy memo" (1x/year).

Axis 2: Isolated (Context Boundary)

This is the most frequent point of failure. "Isolation" refers to the dependencies required to complete the task.

Does the task require information that exists outside the agent's context window or RAG retrieval capabilities?

  • Isolated: "Summarize this PDF." The input is self-contained.
  • Entangled: "Update the legacy billing code." This requires knowledge of undocumented dependencies, historical quirks, and "tribal knowledge" not present in the repo.

The Context Dependency Graph:

[Input Data] ──► [ Agent ] ──► [ Output ]
                     ▲
                     │
            (Context Boundary)
                     │
          [ Tribal Knowledge ] (UNREACHABLE)
          [ Offline Politics ] (UNREACHABLE)

If the dependency graph crosses the context boundary into "implicit knowledge," the agent will hallucinate a plausible but incorrect solution.

Axis 3: Predictable (Deterministic Evaluation)

Can the output be mathematically verified?

  • High Predictability: "Convert JSON to SQL." The SQL either executes or it doesn't. A validator can catch errors.
  • Low Predictability: "Write a polite email to an angry client." "Polite" is subjective. "Angry" is nuanced. There is no unit test for "tone."

Conclusion: The Operator Mindset

Successful AI implementation resembles systems engineering ("The Operator") rather than organizational management ("The Leader").

Don't assign an agent a "job." Assign it a function.

  1. Decompose the role into atomic tasks.
  2. Filter tasks through the RIP matrix.
  3. Build rigid pipelines for the survivors.
  4. Keep humans on the rest.