Compiling Thought: Building a Prompt Compiler for Self-Improving AI

# config/steps.yaml
defaults:
  - _self_
  - logging/json_logger
  - db: postgres
  - agents/step_compiler
  - agents/step_processor
  - agents/prompt_tuning
  - agents/dspy_assembler

goal:
  goal_text: I want to build an AI that can teach itself to solve complex problems better over time.
  goal_type: "tactical"
  goal_category: "meta_learning"
  focus_area: "self_improvement"
  strategy: "stepwise_decomposition"
  difficulty: "medium-high"
  expected_formats:
    - "symbolic_prompt_plan"
    - "stepwise_reasoning_trace"
    - "annotated_score_vector"

paths:
  prompts: ${hydra:runtime.cwd}/prompts

report:
  generate_report: true
  path: ${hydra:runtime.cwd}/reports

embeddings:
  model: "mxbai-embed-large"
  dimension: 1024
  endpoint: "http://localhost:11434/api/embeddings"

pipeline:
  name: default_pipeline
  description: "Default hypothesis generation and refinement pipeline"
  stages:
    - name: step_compiler
      description: "Generates steps from initial goal"
      cls: co_ai.agents.compiler.step_compiler.StepCompilerAgent
      enabled: true
      iterations: 1
    - name: step_processor
      description: "Processes each step to generate and score the outputs"
      cls: co_ai.agents.compiler.step_processor.StepProcessorAgent 
      enabled: true
      iterations: 1
    - name: prompt_tuning
      description: "Tunes prompts in the previous step(s) for better performance"
      cls: co_ai.agents.compiler.prompt_tuning.PromptTuningAgent
      enabled: true
      iterations: 1
    - name: dspy_assembler
      description: "Assembles the prompts using the scores into a single good prompt"
      cls: co_ai.agents.compiler.dspy_assembler.DSPyAssemblerAgent
      enabled: true
      iterations: 1

1. Analyze past reasoning steps   to uncover errors and recurring patterns.
2. Quantify decision confidence   to trigger corrections based on low-certainty steps.
3. Adjust prompt templates   using past successes to refine how we ask questions.

class StepCompilerAgent(ScoringMixin, MemoryAwareMixin, BaseAgent):
    """
    Breaks down a high-level goal into symbolic reasoning steps.
    Each step is a SymbolicNode with step_id, action, description, etc.
    """

    def __init__(self, cfg, memory, logger):
        super().__init__(cfg, memory, logger)
        self.scorer = MRQScorer(cfg, memory=memory, logger=logger)
        self.scorer.load_models()

    async def run(self, context: dict) -> dict:
        # Inject memory-enhanced context
        goal = context.get("goal")
        context = self.inject_memory_context(
            goal=goal, context=context, tags=["step", "plan"]
        )
        prompt = self.prompt_loader.load_prompt(self.cfg, context=context)

        # Call LLM to get a plan
        response = self.call_llm(prompt, context=context)
        steps = self.parse_response_into_steps(response)

        # Store parsed steps
        context["step_plan"] = steps

        # Optional: Score the overall plan
        score_result = self.score_hypothesis(
            {"text": response}, context, metrics="step_reasoning", scorer=self.scorer,
        )
        context["step_plan_score"] = score_result.aggregate()
        context.setdefault("dimension_scores", {})["step_plan"] = score_result.to_dict()

        # Log trace for memory reuse
        self.add_to_shared_memory(
            context,
            {
                "agent": "step_compiler",
                "trace": "\n".join([s["description"] for s in steps]),
                "response": response,
                "score": context["step_plan_score"],
                "dimension_scores": score_result.to_dict(),
                "tags": ["step", "plan"],
            },
        )

        return context

    def parse_response_into_steps(self, response: str):
        """
        Parses a multi-line LLM response into a list of symbolic steps.
        Each step becomes a SymbolicNode (or plain dictionary if symbolic layer is deferred).
        """
        lines = [line.strip() for line in response.strip().splitlines() if line.strip()]
        steps = []
        for i, line in enumerate(lines):
            if ":" in line:
                _, description = line.split(":", 1)
                step = SymbolicNode(
                    step_name=f"step_{i + 1}",
                    action="reasoning_step",
                    description=description.strip(),
                    metadata={"source": "step_compiler"},
                )
                steps.append(asdict(step))
        return steps

# BEFORE Step Compiler  
prompt = "Build self-improving AI"  

# AFTER Step Compiler  
  metadata:
    source: step_compiler
- step_name: step_19
  action: reasoning_step
  thought: null
  prompt: null
  description: 'Tailoring prompts directs reasoning toward goals, reducing redundant
    computation. *Template*: "Iteratively refine prompts using reflection insights
    to prioritize relevant heuristics or constraints." *Impact/Generality*: Medium
    impact (task-dependent), high generality (reusable across prompt-based systems).'

class StepProcessorAgent(ScoringMixin, MemoryAwareMixin, BaseAgent):
    """
    Executes each reasoning step (SymbolicNode) from StepCompilerAgent,
    producing outputs and optionally scoring them.
    """

    def __init__(self, cfg, memory, logger):
        super().__init__(cfg, memory, logger)
        self.scorer = MRQScorer(cfg, memory, logger)
        self.scorer.load_models()

    async def run(self, context: dict) -> dict:
        goal = context["goal"]
        steps = context.get(self.input_key, [])  # input_key: step_plan
        step_outputs = []

        for i, step in enumerate(steps):
            step_context = {
                "goal": goal,
                "step": step["description"],
                "step_index": i,
                **context,  # Include all existing context
            }
            prompt = self.prompt_loader.load_prompt(self.cfg, context=step_context)
            output = self.call_llm(prompt, context=step_context)

            # Score (optional)
            score_result = self.score_hypothesis(
                {"text": output}, context, metrics="step_quality", scorer=self.scorer
            )
            total_score = score_result.aggregate()

            step_outputs.append(
                {
                    "step": step["description"],
                    "output": output,
                    "score": total_score,
                    "dimension_scores": score_result.to_dict(),
                }
            )

            self.logger.log(
                "StepProcessed",
                {"step": step["description"], "output": output, "score": total_score},
            )

        context["step_outputs"] = step_outputs
        return context

"step_outputs": [
  {
    "step": "Introduce a self-evaluation loop",
    "output": "Use a scoring function to assess hypotheses after each generation...",
    "score": 0.82,
    "dimension_scores": {
      "clarity": 0.9,
      "correctness": 0.8,
      "relevance": 0.75
    }
  },
  ...
]

import dspy
from dspy import (BootstrapFewShot, Example, InputField, OutputField, Predict,
                  Signature)

from co_ai.agents.base_agent import BaseAgent
from co_ai.constants import GOAL
from co_ai.scoring.mrq_scorer import MRQScorer

# DSPy signature for prompt refinement: defines input/output fields for tuning
class PromptTuningSignature(Signature):
    goal = InputField(desc="Scientific research goal or question")
    input_prompt = InputField(desc="Original prompt used to generate hypotheses")
    hypotheses = InputField(desc="Best hypothesis generated")
    review = InputField(desc="Expert review of the hypothesis")
    score = InputField(desc="Numeric score evaluating the hypothesis quality")
    refined_prompt = OutputField(desc="Improved version of the original prompt")


# Simple evaluation result class to return from evaluator
class EvaluationResult:
    def __init__(self, score: float, reason: str):
        self.score = score
        self.reason = reason


# Base evaluator interface (not used directly, but useful for future extensions)
class BaseEvaluator(ABC):
    @abstractmethod
    def evaluate(
        self, original: str, proposal: str, metadata: dict = None
    ) -> EvaluationResult:
        pass


# DSPy-based evaluator that can run a Chain-of-Thought program
class DSPyEvaluator(BaseEvaluator):
    def __init__(self):
        self.program = dspy.ChainOfThought(PromptTuningSignature)

    def evaluate(
        self, original: str, proposal: str, metadata: dict = None
    ) -> EvaluationResult:
        result = self.program(
            goal=metadata["goal"],
            input_prompt=original,
            hypotheses=metadata["hypotheses"],
            review=metadata.get("review", ""),
            score=metadata.get("score", 750),
        )
        try:
            score = float(result.score)
        except (ValueError, TypeError):
            score = 0.0
        return EvaluationResult(score=score, reason=result.explanation)


# Main agent class responsible for training and tuning prompts using DSPy
class PromptTuningAgent(BaseAgent):
    def __init__(self, cfg, memory=None, logger=None):
        super().__init__(cfg, memory, logger)
        self.agent_name = cfg.get("name", "prompt_tuning")
        self.prompt_key = cfg.get("prompt_key", "default")
        self.sample_size = cfg.get("sample_size", 20)
        self.generate_count = cfg.get("generate_count", 10)
        self.current_version = cfg.get("version", 1)

        # Configure DSPy with local LLM (Ollama)
        lm = dspy.LM(
            "ollama_chat/qwen3",
            api_base="http://localhost:11434",
            api_key="",
        )
        dspy.configure(lm=lm)

        self.scorer = MRQScorer(cfg, memory, logger)
        self.scorer.load_models()

    async def run(self, context: dict) -> dict:
        goal = self.extract_goal_text(context.get(GOAL))
        step_outputs = context.get("step_outputs", [])
        if not step_outputs:
            self.logger.log("PromptTuningSkipped", {"reason": "no_steps_found"})
            return context

        self.logger.log("StepPromptTuningStart", {"step_count": len(step_outputs)})

        tuned_steps = []
        for i, step in enumerate(step_outputs):
            try:
                original_prompt = step.get("step")  # fallback
                original_output = step["output"]
                original_score = step.get("score", 1000)

                self.logger.log("TuningInputExample", {
                    "goal": goal,
                    "input_prompt": original_prompt,
                    "hypotheses": original_output,
                    "score": original_score
                })

                # Run DSPy prompt tuning program
                example = Example(
                    goal=goal,
                    input_prompt=original_prompt,
                    hypotheses=original_output,
                    review="",  # or step.get("review", "")
                    score=original_score,
                ).with_inputs("goal", "input_prompt", "hypotheses", "review", "score")

                # Generate refined prompt candidates
                program = Predict(PromptTuningSignature)
                tuned_program = BootstrapFewShot(metric=lambda e, p, _: 1.0).compile(
                    student=program,
                    trainset=[example]  # one-shot tuning
                )

                result = tuned_program(
                    goal=goal,
                    input_prompt=original_prompt,
                    hypotheses=original_output,
                    review="",
                    score=original_score,
                )
                self.logger.log("TunedProgramResult", {"step_index": i, "result": str(result)})
                print(f"Refined prompt: {result}")
                refined_prompt = result.refined_prompt.strip()


                # Score both versions
                prompt_a = original_prompt
                prompt_b = refined_prompt

                score_a = self._prompt_quality_metric(
                    example=example,
                    pred=type("obj", (object,), {"refined_prompt": prompt_a}),
                    context=context,
                )
                score_b = self._prompt_quality_metric(
                    example=example,
                    pred=type("obj", (object,), {"refined_prompt": prompt_b}),
                    context=context,
                )

                # Choose best
                best_prompt = prompt_b if score_b > score_a else prompt_a
                best_score = max(score_a, score_b)

                step["refined_prompt"] = best_prompt
                step["refinement_history"] = {
                    "original": prompt_a,
                    "refined_candidate": prompt_b,
                    "score_a": score_a,
                    "score_b": score_b,
                    "selected": best_prompt,
                }
                step["score"] = best_score

                # Store in prompt memory (if needed)
                self.memory.prompt.save(
                    goal={"goal_text": goal},
                    agent_name=self.name,
                    prompt_key=f"step_{i}",
                    prompt_text=best_prompt,
                    response=None,
                    pipeline_run_id=context.get("pipeline_run_id"),
                    strategy="step_prompt_refined",
                    version=self.current_version + 1,
                )

                tuned_steps.append(step)

                self.logger.log("StepPromptRefined", {
                    "step_index": i,
                    "original_snippet": prompt_a[:100],
                    "refined_snippet": prompt_b[:100],
                    "winner": "B" if score_b > score_a else "A",
                    "score_a": score_a,
                    "score_b": score_b,
                })

            except Exception as e:
                self.logger.log("StepPromptTuningFailed", {
                    "step_index": i,
                    "error": str(e),
                    "step_snippet": str(step)[:100],
                })

        context["step_outputs"] = tuned_steps
        self.logger.log("StepPromptTuningCompleted", {"count": len(tuned_steps)})

        return context

    async def generate_and_store_refined_prompts(
        self, tuned_program, goal: str, context: dict, val_set
    ):
        """
        Generate refined prompts using the tuned DSPy program and store them in the database.

        Args:
            tuned_program: A compiled DSPy program capable of generating refined prompts.
            goal: The scientific goal for this run.
            context: Shared pipeline state.
            val_set: Validation examples to run through the tuned program.
        """

        stored_count = 0
        for i, example in enumerate(val_set):
            try:
                # Run DSPy program on new example
                result = tuned_program(
                    goal=example["goal"],
                    input_prompt=example["prompt_text"],
                    hypotheses=example["hypothesis_text"],
                    review=example.get("review", ""),
                    score=example.get("elo_rating", 1000),
                )

                self.logger.log("TunedProgramResult", {"step_index": i, "result": str(result)})
                print(f"Refined prompt: {result}")

                # Safely extract refined prompt
                if not result or not hasattr(result, "refined_prompt") or result.refined_prompt is None:
                    raise ValueError("Refined prompt not returned from DSPy program.")

                refined_prompt = result.refined_prompt.strip()

                # Store refined prompt to the DB
                self.memory.prompt.save(
                    goal={"goal_text": example["goal"]},
                    agent_name=self.name,
                    prompt_key=self.prompt_key,
                    prompt_text=refined_prompt,
                    response=None,
                    pipeline_run_id=context.get("pipeline_run_id"),
                    strategy="refined_via_dspy",
                    version=self.current_version + 1,
                )

                stored_count += 1

                # Update context with prompt history
                self.add_to_prompt_history(
                    context, refined_prompt, {"original": example["prompt_text"]}
                )

                self.logger.log(
                    "TunedPromptStored",
                    {"goal": goal, "refined_snippet": refined_prompt[:100]},
                )

            except Exception as e:
                self.logger.log(
                    "StepPromptTuningFailed",
                    {
                        "step_index": i,
                        "error": str(e),
                        "step_snippet": str(example.get("prompt_text", ""))[:100],
                    },
                )
                print(f"❌ Exception: {type(e).__name__}: {e}")

        self.logger.log("StepPromptTuningCompleted", {"count": stored_count})

    def _prompt_quality_metric(self, example, pred, context: dict) -> float:
        """
        Evaluate whether the refined prompt (pred.refined_prompt) is better than the original (example.input_prompt),
        using fast MR.Q-style prediction from memory embeddings when possible.

        Returns:
            1.0 if refined is better
            0.5 if equal
            0.0 if original is better
        """
        try:
            # Extract both prompts
            prompt_a = example.input_prompt.strip()
            prompt_b = pred.refined_prompt.strip() if pred.refined_prompt else ""

            if not prompt_b:
                self.logger.log(
                    "StepPromptTuningFailed",
                    {
                        "step_index": context.get("step_index"),
                        "error": "Refined prompt is empty",
                        "step_snippet": str(context.get("step", ""))[:200],
                    },
                )
                return 0.0

            # Use dimension-aware scorer to get detailed scores
            dimensions = context.get(
                "dimensions", ["correctness", "clarity", "relevance"]
            )

            # Try fast MR.Q prediction via embeddings first
            try:
                score_dict_a = {
                    dim: self.scorer.predict_score_from_prompt(prompt_a, dim)
                    for dim in dimensions
                }
                score_dict_b = {
                    dim: self.scorer.predict_score_from_prompt(prompt_b, dim)
                    for dim in dimensions
                }

                # Weighted comparison
                weighted_score_a = self._score_weighted(score_dict_a)
                weighted_score_b = self._score_weighted(score_dict_b)

                mode = "predicted"

            except Exception as e:
                self.logger.log(
                    "MRQPredictionFailed", {"error": str(e), "fallback": "llm_scoring"}
                )

                # Fallback: Call LLM to generate output and score it
                goal = context.get("goal", {}).get("goal_text", "")
                output_a = self.call_llm(prompt_a, context={"goal": goal})
                output_b = self.call_llm(prompt_b, context={"goal": goal})

                # Score outputs using MR.Q
                score_dict_a = self.scorer.score(
                    goal={"goal_text": goal},
                    hypothesis={"text": output_a},
                    dimensions=dimensions,
                ).to_dict()
                score_dict_b = self.scorer.score(
                    goal={"goal_text": goal},
                    hypothesis={"text": output_b},
                    dimensions=dimensions,
                ).to_dict()

                weighted_score_a = self._score_weighted(score_dict_a)
                weighted_score_b = self._score_weighted(score_dict_b)

                mode = "llm"

            # Log full evaluation result
            self.logger.log(
                "PromptQualityComparison",
                {
                    "mode": mode,
                    "prompt_a_snippet": prompt_a[:100],
                    "prompt_b_snippet": prompt_b[:100],
                    "scores_a": score_dict_a,
                    "scores_b": score_dict_b,
                    "weighted_score_a": weighted_score_a,
                    "weighted_score_b": weighted_score_b,
                    "winner": "B"
                    if weighted_score_b > weighted_score_a
                    else ("A" if weighted_score_b < weighted_score_a else "Tie"),
                },
            )

            # Return binary decision signal
            if weighted_score_b > weighted_score_a:
                return 1.0
            elif weighted_score_b < weighted_score_a:
                return 0.0
            else:
                return 0.5

        except Exception as e:
            self.logger.log(
                "StepPromptTuningFailed",
                {
                    "step_index": context.get("step_index"),
                    "error": str(e),
                    "step_snippet": str(context.get("step", ""))[:200],
                },
            )
            return 0.0

    def _score_weighted(self, dim_scores: dict) -> float:
        """
        Weighted aggregation of dimensional scores.
        Can be customized per task (e.g., science vs storytelling).
        """
        weights = {
            "correctness": 0.4,
            "clarity": 0.3,
            "relevance": 0.2,
            "originality": 0.1,
        }
        return sum(dim_scores.get(k, 0) * w for k, w in weights.items())

{
  "step": "Ask the user to clarify the research scope",
  "output": "Can you specify the intended scientific discipline for your study?",
  "refined_prompt": "Prompt the user to define the scientific field and scope",
  "refinement_history": {
    "original": "Ask the user to clarify the research scope",
    "refined_candidate": "Prompt the user to define the scientific field and scope",
    "score_a": 0.72,
    "score_b": 0.85,
    "selected": "Prompt the user to define the scientific field and scope"
  }
}

# DSPy signature for merging multiple high-quality prompts into a coherent prompt
class PromptMergeSignature(Signature):
    goal = InputField(desc="The original scientific or research goal.")
    prompts = InputField(desc="List of high-quality prompts to intelligently merge.")
    merged_prompt = OutputField(
        desc="A coherent merged prompt that integrates the best aspects of the provided prompts."
    )


# DSPy module implementing intelligent merging of prompts
class PromptMerger(dspy.Module):
    def __init__(self):
        super().__init__()
        self.merger = ChainOfThought(PromptMergeSignature)

    def forward(self, goal: str, prompts: list[str]) -> dspy.Prediction:
        prompt_text = "\n\n---\n\n".join([f"Prompt {i+1}:\n{p}" for i, p in enumerate(prompts)])
        return self.merger(goal=goal, prompts=prompt_text)


class DSPyAssemblerAgent(ScoringMixin, MemoryAwareMixin, BaseAgent):
    """
    DSPyAssembler uses DSPy to merge and refine multiple prompt variants into one optimal prompt.
    """

    def __init__(self, cfg, memory=None, logger=None):
        super().__init__(cfg, memory, logger)
        self.scorer = MRQScorer(cfg, memory, logger)
        self.scorer.load_models()

        # Configure local LLM (e.g., Ollama)
        self.lm = dspy.LM(
            "ollama_chat/qwen3", api_base="http://localhost:11434", api_key=""
        )
        dspy.configure(lm=self.lm)

        # Initialize modules
        self.prompt_merger = PromptMerger()
        self.mrq_eval_metric = self._mrq_eval_metric
        self.max_included = cfg.get("max_included", 15)

    async def run(self, context: dict) -> dict:
        goal = self.extract_goal_text(context.get(GOAL))
        step_outputs = context.get("step_outputs", [])

        if not step_outputs:
            self.logger.log("PromptTuningSkipped", {"reason": "no_steps_found"})
            return context

        self.logger.log("StepPromptTuningStart", {"step_count": len(step_outputs)})

        # 1. Get top N prompts by score
        ranked_prompts = sorted(
            [step for step in step_outputs if step.get("score")],
            key=lambda x: x["score"],
            reverse=True,
        )

        top_prompts = [
            step.get("refined_prompt") or step.get("prompt")
            for step in ranked_prompts[:self.max_included]
        ]
        top_prompts = [p.strip() for p in top_prompts if p]

        # 2. Create training examples from top-performing prompts
        train_examples = [
            Example(
                goal=goal,
                prompts=top_prompts,
                merged_prompt="",  # Will be filled during tuning
            ).with_inputs("goal", "prompts")
            for _ in range(5)  # Generate 5 example sets
        ]

        # Wrap our scoring metric so we can inject context during tuning
        def wrapped_metric(example, pred, trace=None):
            return self.mrq_eval_metric(example, pred, trace, context=context)

        # 3. Compile tuned merger with BootstrapFewShot
        tuner = BootstrapFewShot(metric=wrapped_metric, max_bootstrapped_demos=4)
        compiled_merger = tuner.compile(self.prompt_merger, trainset=train_examples)

        # 4. Merge prompts using trained module
        try:
            merged_prediction = compiled_merger(goal=goal, prompts=top_prompts)
            merged_prompt = merged_prediction.merged_prompt.strip()
        except Exception as e:
            self.logger.log("PromptMergeFailed", {"error": str(e)})
            merged_prompt = top_prompts[0]  # Fallback to best known prompt

        # 5. Score merged result
        try:
            hypothesis = self.call_llm(merged_prompt, context)
            score_bundle = self.score_hypothesis(
                {"text": hypothesis}, context, metrics="compiler", scorer=self.scorer
            )
            final_score = score_bundle.aggregate()
        except Exception as e:
            self.logger.log("FinalScoreFailed", {"error": str(e)})
            final_score = 0.0

        # 6. Save to context
        context["final_merged_prompt"] = merged_prompt
        context["final_score"] = final_score

        self.logger.log(
            "PromptMergeCompleted",
            {"merged_prompt_snippet": merged_prompt[:200], "final_score": final_score},
        )

        return context

    def _mrq_eval_metric(self, example, pred, trace=None, context=None):
        """Evaluation metric using MR.Q scorer"""
        try:
            merged_prompt = pred.merged_prompt
            hypothesis = self.call_llm(merged_prompt, context=context)
            score_bundle = self.score_hypothesis(
                {"text": hypothesis}, context, metrics="compiler", scorer=self.scorer
            )
            aggregate_score = score_bundle.aggregate()
            normalized_score = aggregate_score / 100.0  # Normalize to [0, 1]
            return normalized_score
        except Exception as e:
            self.logger.log("MRQEvalMetricError", {"error": str(e)})
            return 0.0

**Design a self-improving AI system** with four core mechanisms:  
1. **Reflection Layer** (Impact: 9, Generality: 8)  
  - Log all reasoning steps and use a meta-layer to analyze errors, biases, and inefficiencies. Update knowledge bases with extracted patterns.  
2. **Self-Evaluation Framework** (Impact: 10, Generality: 7)  
  - Assign confidence scores to decisions based on evidence quality and consistency. Trigger corrective actions (e.g., retraining, prompt adjustments) for low-confidence outputs.  
3. **Prompt Tuning** (Impact: 8, Generality: 9)  
  - Dynamically optimize input prompts using reinforcement learning, generating alternative formulations and selecting high-impact variants for future tasks.  
4. **Memory Reuse** (Impact: 9, Generality: 8)  
  - Store structured summaries of past reasoning steps in a memory bank, enabling retrieval of insights for similar tasks while updating memory with new knowledge.  
  
**Integration**:  
- Use dynamic confidence scoring and error-correction protocols to refine mechanisms iteratively.  
- Prioritize cross-dimensional synergy: e.g., memory reuse informs prompt tuning, and self-evaluation drives reflection.  
- Create feedback loops where evaluation results adjust strategies, enabling the system to solve increasingly complex, novel problems over time.

I want to build an AI that can teach itself to solve complex problems better over time.

### 🧠 Step-by-Step Implementation Plan

#### **1. Reflection Layer**

**Purpose**: Analyze reasoning errors and adapt reasoning strategies over time.

**Implementation**:

* ✅ Use your `PipelineJudgeAgent` to log scores and rationale from each reasoning output.
* 🔁 Build a `ReflectionAgent` that periodically reviews these logs (e.g., nightly batch or after a threshold of failures).
* 🧠 Output: Suggested rule updates, prompt rewrites, or agent strategy changes.
* 🛠 Store reflection outputs in `symbolic_rules` for downstream integration.

---

#### **2. Self-Evaluation Framework**

**Purpose**: Assess quality of outputs in real time using dimensional scores (e.g., correctness, originality).

**Implementation**:

* ✅ Use your `ScoringManager` with MR.Q and LLM fallback.
* ➕ Add `ConfidenceEvaluatorAgent` that:

  * Checks for low-scoring outputs.
  * Triggers mutation agents (e.g., `PromptMutator`, `RuleRefiner`) to rerun or adapt strategies.
* 📊 Track confidence scores per step/hypothesis in your `scores` table.

---

#### **3. Prompt Tuning**

**Purpose**: Improve prompts over time to better guide LLM reasoning.

**Implementation**:

* ✅ Leverage `PromptCompilerAgent` with mutation + repair loops.
* ➕ Connect it to your `ScoringManager` to:

  * Compare original vs. mutated prompt quality.
  * Keep the better one using a binary or weighted decision logic.
* 🧪 Train a `MetaPromptSelector` using past performance to pre-rank prompt variants for new tasks.

---

#### **4. Memory Reuse**

**Purpose**: Store and retrieve useful reasoning traces for future problems.

**Implementation**:

* ✅ Use `MemoryAwareMixin` to:

  * Save `SymbolicNode` traces from previous step plans.
  * Attach scores, metadata, and tags.
* 🔍 Implement `ProximityAgent` to fetch similar goals/contexts.
* 🧩 Inject memory into prompt templates using `{{ memory.shared }}`.
* ➕ Build a `MemoryTrainer` that filters and summarizes memory traces for training compact retrievers.

---

### 🔁 Feedback Loops and Integration

* **Use Scores to Drive Loops**:

  * Every agent that produces or mutates content routes through the `ScoringManager`.
  * Low scores → auto-trigger repair/mutation agents.
  * High scores → log and store for reuse or tuning.

* **Cross-Link Dimensions**:

  * Example: `PromptTuningAgent` uses memory-fetched examples from similar past prompts.
  * `ReflectionAgent` suggests scoring strategy updates (e.g., change weights).

* **Symbolic Rule Coordination**:

  * Each mechanism (reflection, prompt tuning, etc.) modifies or applies symbolic rules.
  * Track `rule_effectiveness` over time to evolve best practices.

---

### 🧪 Example Execution Flow

1. **Goal comes in** (e.g., "How can an AI teach itself math?")
2. `StepCompilerAgent` creates symbolic steps.
3. `StepProcessorAgent` executes and scores steps.
4. `PromptCompilerAgent` mutates low-scoring prompts.
5. `DSPyAssemblerAgent` merges best-performing prompt variants.
6. `PipelineJudgeAgent` scores final output.
7. `ReflectionAgent` logs issues or improvements.
8. Memory updated with all symbolic traces + scores.

---

### 🧩 Final Notes

* You already have **most infrastructure built**   now focus on **tight integration**, **trigger rules**, and **self-evaluating loops**.
* Consider defining a **`SelfImprovementPipeline`** in YAML that strings together:

  * `step_compiler → step_processor → prompt_mutator → assembler → judge → reflect`.

Would you like help drafting that YAML pipeline or configuring auto-trigger logic for failures or low scores?