Project 28: Cognitive Prompt Architecture Workbench

A prompt-state framework for working memory, goal stack control, and metacognitive checks.

Quick Reference

Attribute Value
Difficulty Level 3: Advanced
Time Estimate 5-7 days
Main Programming Language TypeScript
Alternative Programming Languages Python, Go
Coolness Level Level 4: Hardcore Tech Flex
Business Potential 3. Service and Support
Knowledge Area Cognitive Architecture Concepts
Software or Tool State model + goal stack runtime
Main Book Clean Architecture

1. Learning Objectives

By completing this project, you will:

  1. Build a production-style artifact for Cognitive Architecture Concepts rather than a toy prototype.
  2. Encode Working memory simulation as explicit, testable runtime behavior.
  3. Integrate Goal stack representation with observable quality/safety/cost controls.
  4. Validate end-to-end behavior with reproducible metrics and failure traces.
  5. Produce documentation and runbooks that make this project operable by a team.

2. All Theory Needed (Per-Concept Breakdown)

Working memory simulation

Fundamentals Working memory simulation defines a deterministic control surface for behavior that is often treated as informal prompt text. In this project, you model it as policy plus evidence: explicit rules, typed outputs, and measurable outcomes. The practical benefit is that you can reason about quality regressions and safety boundaries without reading raw model transcripts every time.

Deep Dive into the concept Treat Working memory simulation as a system-level contract. Start by listing invariants: what must always happen, what must never happen, and what should trigger escalation. Then map each invariant to an observable signal: schema checks, scoring metrics, cost/latency bounds, or risk flags. Next, define failure classes. Avoid one generic “bad output” bucket; classify by root cause so remediations are clear. Finally, connect policy to rollout decisions. If a change improves one metric but harms critical safety or correctness classes, it is not an improvement. The purpose of this concept is not prompt elegance; it is operational predictability.

Goal stack representation

Fundamentals Goal stack representation controls how your system turns uncertain generation into stable software behavior. In mature systems, this includes deterministic gates before and after model calls, explicit state transitions, and reason-coded escalations.

Deep Dive into the concept Implement Goal stack representation as a workflow, not a single prompt. Define preconditions, action steps, postconditions, and rollback behavior. Keep each stage inspectable so failures are attributable. Add guardrails where side effects could occur. Add calibration where confidence or scoring is used. Add bounded retries where transient failures are likely. For every automation step, specify when human review is mandatory. This approach gives you explainability, incident triage speed, and safer operations under real traffic.

Uncertainty and meta-cognition prompts

Fundamentals Uncertainty and meta-cognition prompts provides the optimization or governance layer that keeps the system sustainable over time. It prevents silent drift by forcing periodic re-evaluation and controlled iteration.

Deep Dive into the concept Define a control loop for Uncertainty and meta-cognition prompts: measure, compare to target, decide, and act. Measurements should include quality, latency, cost, and safety signals. Decisions should be policy-backed rather than ad hoc. Actions should be reversible and logged. This loop is where long-term reliability is won: not through one perfect prompt, but through disciplined incremental updates with reproducible evidence.

3. Project Specification

3.1 What You Will Build

You will build a complete Cognitive Architecture Concepts workflow that accepts real request traces, applies deterministic policies, and emits structured outputs plus operational reports. The artifact must be usable by another engineer without tribal context.

3.2 Functional Requirements

  1. Accept batch and single-request executions.
  2. Enforce typed contracts and reason-coded failure outputs.
  3. Capture trace artifacts for every request path.
  4. Produce a summary report with promotion/rejection recommendation.
  5. Support deterministic replay using fixed seeds and config snapshots.

3.3 Non-Functional Requirements

  • Reliability: deterministic behavior for identical inputs and policy versions.
  • Performance: must stay inside configured latency and budget thresholds.
  • Observability: every decision must be traceable and auditable.
  • Safety: high-risk outcomes must follow explicit escalation policies.

3.4 Example Usage / Output

$ npm run p28 -- --session fixtures/long_horizon_task_1.json
[state] goal_stack_depth=4
[metacog] contradiction_checks=PASS
[trace] out/p28/cognitive_state_trace.json

3.5 Real World Outcome

At completion, you have a runnable pipeline that produces three concrete deliverables:

  1. A per-case trace log showing policy decisions and fallback paths.
  2. A machine-readable report consumed by CI/release workflows.
  3. A human-readable operations summary with top failure classes and remediation suggestions.

4. Solution Architecture

4.1 High-Level Design

Input Traffic
   |
   v
Policy/Context Layer -> Model/Tool Execution Layer -> Validation Layer -> Decision Layer
   |                         |                        |                 |
   +-------------------------+------------------------+-----------------+
                             |
                             v
                    Metrics + Trace + Audit Store

4.2 Key Components

  • Ingress Adapter: normalizes requests and attaches metadata.
  • Policy Engine: applies deterministic rules for Working memory simulation.
  • Execution Runtime: runs model/tool steps for Goal stack representation.
  • Validation & Scoring: enforces contracts and quality gates.
  • Decision Router: promote/retry/fallback/escalate outcomes.
  • Observability Layer: stores traces, metrics, and reason codes.

4.3 Data and State Contracts

  • Input contract includes request id, intent/risk metadata, and context bundle.
  • Output contract includes status, typed payload, confidence, citations (if needed), and reason code.
  • State contract captures retries, fallback usage, and escalation actions.

5. Implementation Plan

  1. Phase 1 - Contracts and fixtures: define schemas, reason codes, and minimal replay fixtures.
  2. Phase 2 - Core runtime: implement policy + execution + validation path.
  3. Phase 3 - Recovery and escalation: add retry/fallback/HITL behaviors.
  4. Phase 4 - Benchmarking: run regression suite and baseline comparisons.
  5. Phase 5 - Hardening: add dashboards, alert thresholds, and runbook notes.

6. Validation and Operations

  • Run a deterministic regression suite on every prompt or policy change.
  • Track p50/p95 latency, cost per request, and failure-class rates.
  • Gate release by explicit thresholds and block on critical safety regressions.
  • Keep canary and rollback strategy ready for production rollout.

7. Common Failure Modes and Debugging

Failure 1: “Metrics look good but user trust drops”

  • Why: benchmark underrepresents real hard cases.
  • Fix: include incident-derived fixtures and adversarial slices.
  • Quick test: replay last-30-day incident set and compare deltas.

Failure 2: “Retries increase cost without quality gain”

  • Why: retry strategy is static and repeats same path.
  • Fix: vary strategy by failure class and cap attempts.
  • Quick test: compare recovery rate before/after strategy mutation.

Failure 3: “Escalations are inconsistent”

  • Why: thresholds are ambiguous or drifted.
  • Fix: pin thresholds in policy version and add calibration checks.
  • Quick test: run threshold sensitivity sweep and inspect flip points.

8. Interview Questions They Will Ask

  1. “How do you keep long-horizon agent reasoning coherent across many turns?”
  2. “Which invariants are truly release-blocking in this system?”
  3. “How do you prove improvements are not benchmark overfitting?”
  4. “What failure modes did your architecture reduce the most?”
  5. “How do you transition this project from pilot to production?”

9. Definition of Done

  • Core functionality works on reference inputs with deterministic outputs.
  • Edge cases and adversarial cases are covered in the test suite.
  • Performance, cost, and reliability metrics meet declared thresholds.
  • Failure reasons and escalation behaviors are explicit and reproducible.
  • Runbook notes explain operation, rollback, and troubleshooting steps.