Research Question
How much of the guidance currently encoded as natural-language instructions in agent files can be converted into deterministic, non-LLM decision logic — reducing the cognitive load on the LLM and increasing behavioral reliability? Can delegation itself be modelled as a reusable skill?
Context
The current agent architecture relies heavily on LLM instruction-following: an agent reads a long body of prose instructions and the model must infer the correct action. This is the expensive, drift-prone path. Every token the LLM spends deciding "should I delegate this?" or "which phase gate applies here?" is a token not spent on the actual task.
The insight: skills can encode decision logic, not just procedures. A skill file that includes a full decision tree ("if task type is X → delegate to agent Y; if output file exists → skip phase; if review returns CHANGES_REQUESTED → fix and re-submit") removes that reasoning from the LLM's cognitive budget at runtime.
Three specific hypotheses to test:
-
Delegation as a skill: The Delegation Decision Gate in the Executive Orchestrator is currently a prose lookup table. Could this be a SKILL.md that multiple agents load — pinning the routing logic deterministically and removing it from each agent's instruction body?
-
Phase gate logic as a skill: The per-phase sequence (prune → checkpoint → commit → review → compact) is currently re-stated in each orchestrator instruction set. A phase-gate skill would encode this as a procedure multiple agents share.
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Decision tree offloading: What is the theoretical minimum instruction-body size for an agent if all decision logic is extracted into referenced skills? What remains — identity/persona/values — is the irreducible core.
Scope
- Survey: PDDL and classical AI planning (what can be fully formalized); BDD/Gherkin (precondition/action/postcondition as decision encoding); finite state machines as agent workflow models
- Audit current agent files for recurring decision patterns (delegation routing, phase gates, review cycles) that appear in ≥2 agent bodies — candidates for skill extraction
- Design prototype skill(s) for:
delegation-routing, phase-gate-sequence
- Evaluate: what is the instruction-body size reduction from extracting these into skills? Token budget saved?
Gate Deliverables
Related Issues
Research Question
How much of the guidance currently encoded as natural-language instructions in agent files can be converted into deterministic, non-LLM decision logic — reducing the cognitive load on the LLM and increasing behavioral reliability? Can delegation itself be modelled as a reusable skill?
Context
The current agent architecture relies heavily on LLM instruction-following: an agent reads a long body of prose instructions and the model must infer the correct action. This is the expensive, drift-prone path. Every token the LLM spends deciding "should I delegate this?" or "which phase gate applies here?" is a token not spent on the actual task.
The insight: skills can encode decision logic, not just procedures. A skill file that includes a full decision tree ("if task type is X → delegate to agent Y; if output file exists → skip phase; if review returns CHANGES_REQUESTED → fix and re-submit") removes that reasoning from the LLM's cognitive budget at runtime.
Three specific hypotheses to test:
Delegation as a skill: The Delegation Decision Gate in the Executive Orchestrator is currently a prose lookup table. Could this be a
SKILL.mdthat multiple agents load — pinning the routing logic deterministically and removing it from each agent's instruction body?Phase gate logic as a skill: The per-phase sequence (prune → checkpoint → commit → review → compact) is currently re-stated in each orchestrator instruction set. A
phase-gateskill would encode this as a procedure multiple agents share.Decision tree offloading: What is the theoretical minimum instruction-body size for an agent if all decision logic is extracted into referenced skills? What remains — identity/persona/values — is the irreducible core.
Scope
delegation-routing,phase-gate-sequenceGate Deliverables
delegation-routingSKILL.md prototype with full decision tree for Executive Orchestrator routingphase-gate-sequenceSKILL.md prototype encoding the per-phase sequence as a reusable proceduredocs/research/skills-as-decision-logic.mdRelated Issues
docs/research/values-encoding.mdPattern 3 and 5 (programmatic governance, structural steganography)