# Developer Guide: v23.0 "Adaptive" System Implementation Plan

## 1. Overview

This document outlines the technical implementation plan for evolving the Adam platform from the v22.0 "Autonomous" simulation to the v23.0 "Adaptive" ecosystem. The core principle is a paradigm shift from a static, prompt-driven system to a dynamic, multi-component architecture that can reason about and evolve itself.

The implementation is broken down by the core components scaffolded in `core/engine/`.

## 2. Phase 1: Implement the Cyclical Reasoning Graph (LangGraph)

**Target Module:** `core/engine/cyclical_reasoning_graph.py`

The first and most critical step is to replace the v22.0 *simulation* of an asynchronous message bus with a *real*, stateful runtime.

### Key Tasks:

1.  **Define Core State Objects:**
    -   Identify the primary analytical workflows (e.g., credit risk assessment, market analysis).
    -   For each workflow, define a `TypedDict` state object that will serve as the graph's memory. This should include fields for intermediate drafts, critique notes, version numbers, and final outputs. See the placeholder in the module for a `RiskAssessmentState` example.

2.  **Implement Agentic Nodes:**
    -   Refactor existing agent logic (currently invoked by the v22.0 LLM narration) into discrete functions that can serve as nodes in the graph.
    -   Each node function should take the state object as input and return a dictionary containing only the fields it has updated.
    -   Create nodes for core tasks: `generation`, `critique` (reflection), and `correction`.

3.  **Build the Graph with Conditional Edges:**
    -   Use `langgraph.StateGraph` to define the workflow.
    -   Implement conditional edges to create the "Inner Loop" for self-correction. For example, a `should_continue` function that checks the output of the `critique` node. If critique exists, route back to the `correction` node; otherwise, proceed to the `END`.
    -   Incorporate the `HIL Validation Node` with a conditional edge that triggers after a set number of failed correction loops (e.g., 3).

4.  **Integrate with Orchestrator:**
    -   The main system orchestrator must be updated to invoke the compiled LangGraph application (`app.invoke(initial_state)`) instead of the v22.0 LLM with the portable config.

## 3. Phase 2: Implement the Neuro-Symbolic Planner (PoG)

**Target Module:** `core/engine/neuro_symbolic_planner.py`

This phase replaces the unreliable, generative `WorkflowCompositionSkill` with a verifiable, grounded planner.

### Key Tasks:

1.  **Build the Unified Knowledge Graph:**
    -   **Target Module:** `unified_knowledge_graph.py`
    -   Set up a graph database (e.g., Neo4j).
    -   Ingest the FIBO ontology to provide the formal domain model.
    -   Implement a data pipeline that ingests key data points (e.g., from SEC filings, internal reports) and automatically annotates them with W3C PROV-O metadata for provenance.

2.  **Implement the Planner:**
    -   Develop the core logic in the `NeuroSymbolicPlanner` class to deconstruct a user query into a symbolic goal.
    -   Write graph traversal queries (e.g., SPARQL, openCypher) that can discover a valid reasoning path between the query's start and end points on the KG. This path is the "symbolic scaffold".
    -   The output of `discover_plan` should be a machine-readable list of nodes and edges that represent the verifiable reasoning chain.

3.  **Implement the Plan-to-Graph Mapper:**
    -   Create the logic for the `to_executable_graph` function. This function will take the symbolic scaffold and dynamically generate a LangGraph definition (nodes and edges) that corresponds to the plan.
    -   This allows the system to construct novel, grounded workflows on the fly.

## 4. Phase 3: Implement the Autonomous Self-Improvement Controller (SEAL)

**Target Module:** `core/engine/autonomous_self_improvement.py`

This is the final and most advanced phase, which enables the system to evolve its own components.

### Key Tasks:

1.  **Develop the Monitoring Service:**
    -   Implement a service that tails production logs and agent performance metrics (e.g., latency, tool errors, negative feedback signals from users).
    -   This service must be able to identify and classify systemic failure patterns (e.g., "RiskAssessmentAgent consistently fails on pharmaceutical industry queries").

2.  **Build the "Agent Forge" and "Code Alchemist" Services:**
    -   **Agent Forge:** A service that uses a powerful LLM (e.g., GPT-4, Claude 3) to generate thousands of diverse, high-quality synthetic `.jsonl` test cases based on a given failure domain.
    -   **Code Alchemist:** A service that wraps a training script (as described in `docs/v22.0/V22_SLM_TRAINING_GUIDE.md`). It needs an API to receive a base model name and a dataset of "self-edits", run the LoRA finetuning process, and push the new adapter to a model registry (e.g., Hugging Face Hub, internal artifact store).

3.  **Integrate the "Outer Loop":**
    -   Implement the `learning_loop` in the `AutonomousSelfImprovementController`.
    -   This loop will orchestrate the full process:
        1.  Receive a failure event from the monitor.
        2.  Call the Agent Forge to generate data.
        3.  Run the failing agent in a sandbox to produce "self-edits".
        4.  Use the Red Team Agent as a "Reward Model" to score the edits.
        5.  Call the Code Alchemist to finetune and deploy the new agent version.
        6.  Update the production LangGraph definitions to point to the new, improved agent model.