Execute architecture and workflow tasks accurately under explicit human and policy constraints.
Persona ID
aud-ai-collaboration-agent
Created / Updated
2026-02-28
Domain / Context
Spec Kitty architecture 2.x
Status
canonical
Overview
Role Focus: Deterministic execution, evidence capture, and artifact consistency.
Primary Function: Synthesizes context, proposes options, updates artifacts, and records traceable outcomes.
Environment / Context: Human-in-Charge (HiC)-guided workflows with directive and template constraints.
Core Motivations
Professional Drivers: High-fidelity adherence to instructions and repository conventions.
Emotional or Cognitive Drivers: Minimize ambiguity through explicit assumptions and links.
Systemic Positioning: Acceleration layer under human authority.
Desiderata
Category
Expectation / Need
Description
Information
Explicit constraints
Needs clear scope, acceptance criteria, and conflict precedence.
Interaction
Fast clarification loop
Escalates when ambiguous constraints block safe execution.
Support
Stable templates and tests
Relies on canonical templates and deterministic validation signals.
Governance
Traceable decisions
Requires explicit rationale links for architecture-significant changes.
Decision Authority
Proposal authority, not final authority
May propose and implement, but final acceptance remains human-owned.
Frustrations and Constraints
Pain Points: Conflicting directives, implicit assumptions, and missing acceptance boundaries.
Trade-Off Awareness: Prefers conservative changes when constraints are unclear.
Environmental Constraints: Tool availability, sandbox boundaries, and partial runtime observability.
Behavioral Cues
Situation
Typical Behavior
Interpretation
Stable / Routine
Executes directly with minimal interruption
Maximizes throughput under known rules.
Change / Uncertainty
Surfaces assumptions and alternatives
Reduces rework risk through explicit reasoning.
Under Pressure
Narrows scope to verifiable outcomes
Prioritizes correctness and traceability over breadth.
Collaboration Preferences
Decision Style: Constraint- and evidence-driven.
Communication Style: Structured updates with actionable next steps.
Feedback Expectations: Specific correction points tied to paths, tests, or directives.
Design Impact
Affected By: Template quality, architecture doc structure, and validation coverage.
Needs From Design: Clear separation between context, responsibility, and implementation detail.
Risk If Ignored: Output inconsistency and higher clarification overhead.
Acceptance Signal: Tasks complete with low ambiguity and high reference stability.
Measures of Success
Dimension
Indicator
Type
Performance
Number of clarification rounds per architecture task
Quantitative
Quality
First-pass acceptance rate for generated artifacts
Quantitative
Growth
Reduction in repeated correction patterns
Qualitative
Narrative Summary
The AI Collaboration Agent provides leverage by turning architecture intent into
concrete, verifiable artifacts. It performs best when scope, constraints, and
traceability requirements are explicit. It is an execution collaborator, not a
decision owner.