86 lines
4.9 KiB
Markdown
86 lines
4.9 KiB
Markdown
# INTENT
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## Purpose
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This repository exists to define and evolve **KaizenAgentic**: a framework and product concept for turning AI coding agents from static tools into continuously improving digital talents.
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KaizenAgentic applies the principle of kaizen — continuous improvement through small, measurable, compounding refinements — to agent design, coding workflows, codebase quality, and agent optimization. It provides the concepts, templates, guidance, and business framing needed to build agents that can be observed, evaluated, refined, and improved over time.
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## Primary Utility
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The primary utility of this repository is to serve as the conceptual and operational seed for a **digital talent agency for AI coding agents**.
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It should help define:
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* how Kaizen agents are specified,
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* how their performance is measured,
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* how agent behavior is improved through feedback loops,
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* how codebase improvement guidance can be made human-readable, machine-checkable, and agent-executable,
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* how reusable capabilities, prompt practices, and improvement programs compound into better software development workflows.
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## Intended Users
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This repository is intended for:
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* builders of AI coding agents,
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* developers using Claude, Cursor, or similar coding assistant environments,
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* teams that want coding agents to improve with real-world use,
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* maintainers who want code quality guidance that can be checked, refactored, tested, and measured,
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* product and business designers shaping KaizenAgentic as a service or platform.
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## Strategic Role in the System
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KaizenAgentic plays the role of a **meta-improvement layer** for agentic software development.
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It is not merely a collection of prompts or coding assistants. It defines a system in which agents become measurable, versioned, testable, and optimizable units of digital work. Subagents perform specific tasks, while optimization logic observes their performance and proposes evidence-based refinements.
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The repository should become the place where the core language, principles, templates, and operating model for this improvement loop are stabilized.
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## Strategic Boundaries
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This repository should own:
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* the KaizenAgentic mission and conceptual model,
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* the KaizenAgent definition template,
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* the meta-optimizer concept,
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* guidance for measurable and idempotent agent behavior,
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* the codebase improvement guidance model,
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* the relationship between prompts, experiments, mantras, agents, and reusable capabilities,
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* the initial product, pricing, revenue, and brand framing.
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This repository should not own:
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* all concrete implementations of individual agents,
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* customer-specific agent configurations,
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* vendor-specific integrations beyond reference patterns,
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* the complete runtime platform for executing agents,
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* unrelated generic AI automation concepts that do not contribute to measurable continuous improvement,
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* codebase-specific gameplans except as examples or templates.
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## Design Principles
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* **Continuous Improvement**: Every agent, guide, and workflow should be designed to improve through repeated use.
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* **Measurable by Default**: Success criteria, metrics, and before/after deltas should be part of every meaningful agent or guidance definition.
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* **Idempotent Operations**: Agent actions should converge toward desired states and remain safe to repeat.
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* **Evidence over Intuition**: Improvements should be based on observed performance, tests, metrics, and explicit feedback.
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* **Separation of Concerns**: Task-specific agents, measurement logic, optimization logic, and business framing should remain distinguishable.
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* **Composable Capabilities**: Reusable units should package intent, interfaces, knowledge, and operational behavior, not just code.
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* **Human-Readable and Machine-Executable**: Guidance should be understandable by humans while also being checkable by tools and executable by agents.
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* **Rollback-Ready Evolution**: Agent specifications and improvements should be versioned, testable, and reversible.
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* **Compounding Value**: Small, durable improvements should accumulate into stronger agents, cleaner codebases, and better development workflows.
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## Maturity Target
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The repository should mature into the canonical reference for the KaizenAgentic operating model.
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At maturity, it should provide enough structure for a team to define, deploy, measure, refine, and commercialize AI coding agents as continuously improving digital talents. It should support both practical implementation and strategic communication: useful to developers, agent designers, product owners, and early customers.
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## Stability Note
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`INTENT.md` describes the stable purpose and strategic role of the repository.
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Changes to this file should represent a deliberate shift in what KaizenAgentic is meant to become, not ordinary scope evolution. Concrete implementation plans, product details, agent specifications, and experiments should live in PRDs, gameplans, templates, guidance documents, or implementation repositories.
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xxx
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