Traditional AI coding tools follow: AI writes whatever it can, then checks compliance. This relies on post-hoc checking - once AI outputs code with syntax sugar, implicit behavior bugs already exist.

Dmmsys AI Harness Client reverses this: AI must only write compliant code, no post-hoc checking needed. Through a built-in atomic form constraint knowledge base, AI understands allowed forms and forbidden sugars before output, ensuring code quality at the source.

1)    174 built-in atomic forms covering Python, Dart, Java basic statements, expressions, and control flows.

2)    72 forbidden syntax sugars with desugaring rules, each sugar has a clear simplest-form replacement.

3)    Auto-generated constraint prompts: system prompts with form lists and generation principles injected into AI context by language.

4)    Code purity validation: automatically validates AI output contains only atomic forms, marking violations with specific locations.

5)    Hard enforcement at config level: does not rely on AI self-awareness, code with syntax sugar cannot be written to disk at all.

AI coding is not an isolated one-time operation but a continuous conversation. Session context knowledge graph structures each Q&A round into a searchable knowledge base, enabling relevant historical sessions to be retrieved before subsequent calls.

This means AI remembers what you did before. When you propose a new coding task, AI automatically retrieves relevant historical conversations, understands context, avoids re-asking known information, and provides more coherent assistance.

1)    Complete round recording: user messages, AI responses, reasoning process, model used, deep thinking status all stored.

2)    Deterministic keyword extraction: Chinese/English segmentation plus stopword filtering extracts keywords for indexing, no guessing.

3)    Relevance-based retrieval: new tasks automatically match historical session keywords, sorted by hit count.

4)    Explicit no-match: when no relevant history is found, explicitly returns no-match, never fabricates context.

The most common problem with AI coding assistants is not knowing what code exists in the project. Project code knowledge graph automatically scans source code, structuring files, classes, functions, imports, and call relationships into a searchable knowledge base.

When you propose a coding task, AI automatically retrieves relevant classes and functions, understands their parameters, docs, and locations, and continues work based on existing code structure rather than starting from scratch.

1)    Automatic project scanning: recursively scans source files, parsing into file/class/function nodes with import and call relationships.

2)    Structured knowledge nodes: each function node contains qualified name, parameter list, docstring, line range for complete traceability.

3)    Explicit error recording: parse failures explicitly record error cause and file path, never silently skipped.

4)    Symbol-based retrieval: new tasks auto-decompose keywords to match function and class nodes, sorted by hit count.

5)    Full qualified names: retrieval results include qualified names and file paths, AI can precisely locate code.

Session context and project code knowledge graphs are not isolated but merged through a unified retrieval injection service into a complete memory loop.

Before AI calls, the system auto-retrieves from both graphs and builds context injection prompts. After AI responds, the system writes the round back to the session graph. Next call can retrieve this round, forming a closed loop.

1)    Dual-graph merged retrieval: simultaneously retrieves from session and code graphs, merging results into complete context injection.

2)    Four match states: both hit, session only, code only, neither hit - each with clear handling.

3)    Structured injection text: includes related historical sessions and code structure sections with clear source labels.

4)    Memory loop: retrieve-inject → AI generates → write-back → next retrieval hits, forming a complete cycle.

5)    Never fabricate: when both graphs find nothing, explicitly returns empty injection, never fabricates context.

AI Harness Client supports multiple runtime forms - as a constraint engine for AI coding plugins, or as a standalone tool service for other AI agents.

Regardless of form, core capabilities are identical: atomic form constraints, code purity validation, constraint prompt generation. Enterprises can choose the integration method that fits their tech stack.

1)    Plugin form: serves as an AI coding assistant plugin providing constraint-aware coding, AI generates code within constraint space.

2)    Tool service form: serves as a standalone tool service via standard protocol for other AI agents to call.

3)    Hard enforcement interception: auto-validates code purity on file write, blocks syntax sugar at config level.

4)    Three language support: Python, Dart, Java fully covered with complete atomic forms and syntax sugar rules.

AI Harness Client follows strict design principles ensuring every line of code is reliable, auditable, and traceable.

These principles are not slogans but design decisions implemented in every feature, guaranteeing AI coding assistance quality.

1)    Never fallback: when both graphs find nothing, explicitly returns no-match with empty prompt, never fabricates context.

2)    Never abbreviate: all variable/method/table names contain complete business semantic words, injection text includes full qualified names.

3)    Deterministic retrieval: keyword extraction is rule-based (segmentation plus stopword filtering), not guessing; sorted by keyword hit count.

4)    Explicit error audit: code scan failures explicitly record error cause and file path, never silently skipped.