Project 21: Adaptive Autonomy Engine (Learning From Feedback)

Build a controlled adaptation loop where user feedback updates assistant behavior while preserving safety, stability, and rollback ability.

Quick Reference

Attribute Value
Difficulty Level 5: Master
Time Estimate 30-50 hours
Main Programming Language Python
Alternative Programming Languages TypeScript, Go
Coolness Level Level 5: Pure Magic
Business Potential 4. The “Open Core” Infrastructure
Prerequisites experimentation methods, policy versioning, behavioral telemetry
Key Topics reward modeling, preference learning, adaptive prompting, behavior scoring

1. Learning Objectives

  1. Capture explicit and implicit user feedback signals.
  2. Convert feedback into reward and behavior scores.
  3. Deploy adaptive prompt/policy variants safely.
  4. Run shadow evaluations before full rollout.
  5. Roll back harmful adaptations quickly.

2. Theoretical Foundation

2.1 Adaptation as a Controlled Feedback System

Adaptive assistants need feedback loops with bounded updates. Without bounds, systems oscillate or overfit. Good adaptation uses versioned policies, evidence thresholds, and rollback criteria.

2.2 Preference Learning Boundaries

Users can have inconsistent preferences depending on context. Effective personalization must model context and uncertainty instead of blindly optimizing global averages.

3. Project Specification

3.1 What You Will Build

An adaptation service with:

  • feedback collector
  • reward model
  • policy variant manager
  • A/B evaluator
  • rollback controller

3.2 Functional Requirements

  1. Ingest explicit ratings and implicit behavior signals.
  2. Compute behavior score per policy version.
  3. Generate candidate adaptation variants.
  4. Evaluate variants in shadow mode.
  5. Promote or rollback based on guardrail metrics.

3.3 Non-Functional Requirements

  • Safety: hard bounds on policy drift.
  • Auditability: full adaptation history.
  • Fairness: no cross-user preference leakage.

3.4 Real World Outcome

$ adaptctl train --window 30d --user u-119
[Signals] explicit=124 implicit=412
[RewardModel] weights updated (brevity +0.14, risk_tolerance -0.11)
[Deploy] policy_v33 shadow=true
[A/B] satisfaction +7.8% error_rate +0.3%
[Decision] promote with tighter risk cap

4. Solution Architecture

4.1 High-Level Design

Feedback Stream -> Feature Builder -> Reward Model -> Policy Variant -> Shadow Eval -> Promote/Rollback

4.2 Key Components

Component Responsibility Key Decisions
Feedback collector gather signals schema consistency
Reward model score outcomes weighted feature set
Variant manager policy versions bounded update rules
Rollback controller safety fallback automatic triggers

5. Implementation Guide

5.1 The Core Question You’re Answering

“How do I make assistants improve from user feedback without becoming unstable or unsafe?”

5.2 Concepts You Must Understand First

  1. Reward modeling intuition
  2. A/B and shadow deployment patterns
  3. Preference drift handling
  4. Policy versioning and rollback

5.3 Questions to Guide Your Design

  1. Which signals are trustworthy enough to optimize?
  2. How do you avoid overfitting to one noisy period?
  3. Which metrics force rollback automatically?

5.4 Thinking Exercise

Design adaptation behavior for a user who alternates between “short” and “detailed” responses by context.

5.5 The Interview Questions They’ll Ask

  1. What distinguishes personalization from overfitting?
  2. How do you design reward signals for conversational quality?
  3. How do you detect harmful behavior drift?
  4. What should a safe rollout process include?
  5. How do you roll back quickly after degradation?

5.6 Hints in Layers

Hint 1: begin with read-only feedback logging.

Hint 2: add shadow-mode policy updates first.

Hint 3: cap update magnitude per iteration.

Hint 4: define rollback criteria before first promotion.

5.7 Books That Will Help

Topic Book Chapter
Iterative AI systems “AI Engineering” iteration chapters
Product feedback loops “The Pragmatic Programmer” feedback sections
Preference modeling context InstructGPT paper reward model sections

5.8 Common Pitfalls and Debugging

Problem 1: behavior oscillation

  • Why: updates too sensitive to short-term noise.
  • Fix: smoothing windows and minimum evidence thresholds.
  • Quick test: noisy simulation remains stable.

Problem 2: adaptation harms reliability

  • Why: no hard guardrails during promotion.
  • Fix: enforce safety and regression gates.
  • Quick test: policy promotion blocked on metric degradation.

5.9 Definition of Done

  • Feedback ingestion supports explicit + implicit signals
  • Adaptation is versioned, bounded, and auditable
  • Shadow evaluation governs promotion
  • Rollback automation is tested