bitter-lesson-scorecard

name

bitter-lesson-scorecard

description

Score an agent system design against the Bitter Lesson principle — how much "how" is encoded vs "what", how much bets on model improvement vs locks in current limitations. Flags procedural lock-in, hardcoded orchestration, and domain hacks. Produces a simplification roadmap. Use when designing new agent systems, reviewing agent architecture, or deciding what to simplify. Trigger on "bitter lesson", "score architecture", "agent complexity audit", "simplification roadmap", "how vs what ratio", "are we fighting the model".

Bitter Lesson Scorecard for Agent Architectures

Score an agent system against the Bitter Lesson: computation and learning beat hand-engineering. Systems that encode "how" instead of "what" get worse as models improve.

Phase 1: Intake

Ask the user to provide ONE of:

• An agent system description or architecture doc
• A codebase path (will scan for orchestration patterns)
• A CLAUDE.md or system prompt describing an agent workflow

If given a codebase path, look for: orchestration files, state machines, routing logic, prompt templates, tool definitions, multi-agent coordination code.

Phase 2: Component Inventory

Map every architectural component into one of these categories:

Category	Bitter Lesson Alignment	Examples
Outcome Spec	ALIGNED — says "what"	Goal definitions, success criteria, quality thresholds
Tool Interface	ALIGNED — extends capability	API wrappers, file access, search tools
Hard Constraint	NEUTRAL — business necessity	Auth, rate limits, compliance rules, safety gates
Procedural Orchestration	MISALIGNED — encodes "how"	State machines, fixed step sequences, hardcoded agent routing
Model Compensation	MISALIGNED — bets against improvement	Chunking strategies, re-ranking, format enforcement, retry heuristics
Domain Hack	MISALIGNED — freezes current knowledge	Hardcoded few-shot examples, domain-specific parsing, manual entity extraction

Phase 3: Scoring

Calculate three scores (0-100):

Alignment Score

(outcome_specs + tool_interfaces + hard_constraints) / total_components * 100

Higher = more aligned with the Bitter Lesson.

Lock-In Score

(procedural_orchestration + model_compensation + domain_hacks) / total_components * 100

Higher = more locked into current model limitations. This is the number to REDUCE.

Improvement Leverage

Estimate: if the underlying model improves 2x in capability, what percentage of the system becomes unnecessary?

High leverage = the system will naturally simplify with better models. Low leverage = the system fights improvement.

Phase 4: Scorecard Output

## Bitter Lesson Scorecard: [System Name]

| Metric | Score | Rating |
|--------|-------|--------|
| Alignment | XX/100 | [STRONG/MODERATE/WEAK] |
| Lock-In | XX/100 | [LOW/MODERATE/HIGH] |
| Improvement Leverage | XX% | [HIGH/MODERATE/LOW] |

## Component Breakdown
| Category | Count | % of System |
|----------|-------|-------------|
| Outcome Specs | N | X% |
| Tool Interfaces | N | X% |
| Hard Constraints | N | X% |
| Procedural Orchestration | N | X% |
| Model Compensation | N | X% |
| Domain Hacks | N | X% |

## Top Bitter Lesson Violations
1. [Component] — [Why it bets against improvement]
2. ...
3. ...

## Simplification Roadmap
### Quick wins (delete now, test)
- ...

### Medium-term (replace orchestration with outcome specs)
- ...

### Strategic (requires model capability validation)
- ...

Phase 5: Recommendations

For each MISALIGNED component, suggest:

1. What it would look like as an outcome spec instead of a procedure
2. What model capability would need to exist to delete it
3. Whether that capability likely already exists in current models

Verification

• Every component in the system must be classified (nothing skipped)
• Scores must be mathematically consistent with component counts
• Simplification roadmap must have at least one item in each tier
• Recommendations must be specific to the system, not generic advice

Source

Nate's Newsletter (2026-04-01): The Bitter Lesson applied as a practical audit tool for agent architectures — scoring systems on "how" vs "what" encoding and producing simplification roadmaps.