navveenb Bloat Detector
Scan a codebase or system description for the 7 types of agentic bloat and produce a scored audit.
Lean Principle
- #1 Context is a liability
- #2 Right-size your model
- #3 Orchestration is not a playground
- #4 Reflections cost compute
- #5 RAG isn't always right
- #7 Reuse over recompute
- #8 More tools, more problems
Instructions
Step 1: Scan for each bloat type
Use Grep and Read to look for evidence of these 7 bloat patterns:
1. Model Bloat — Look for hardcoded frontier model names (gpt-4, claude-opus, gemini-ultra) used for simple tasks (classification, extraction, formatting).
2. Orchestration Bloat — Count distinct agent definitions, multi-agent chains, sequential agent invocations. Flag if more than 3 agents are chained sequentially.
3. Reflection Bloat — Look for self-critique patterns: "review your output", "improve your answer", retry loops, while loops around LLM calls without exit conditions.
4. Context Bloat — Look for large system prompts (>2000 chars), full document injection, unbounded conversation history loading, many tool descriptions.
5. Tool Bloat — Count tool/function definitions per agent. Flag agents with more than 5 tools.
6. Memory Bloat — Look for unbounded storage: missing TTLs, "store everything", no cleanup/expiry logic, full transcript storage.
7. Retrieval Bloat — Look for RAG pipelines that run on every query without gating: always-on vector search, no relevance thresholds, no caching of retrieval results.
Step 2: Score each category
Score each bloat type 0-3:
- 0 = No evidence of bloat
- 1 = Minor (some instances, manageable)
- 2 = Moderate (multiple instances, should optimize)
- 3 = Severe (systemic, causing significant waste)
Step 3: Write report
Write a report to reports/bloat-audit.md with:
- Score per category (0-3)
- Total score out of 21
- Specific file:line references for each finding
- Severity rating: 0-5 Lean, 6-12 Moderate bloat, 13-21 Severe bloat
- Top 3 recommended fixes
Output
Report saved to reports/bloat-audit.md
Example
Input: A project with 5 chained agents all using GPT-4, no caching, 10 tools per agent. Output:
Model Bloat: 3/3 — All 5 agents use frontier model for mixed tasks
Orchestration Bloat: 2/3 — 5 agents chained, 2 could be merged
Tool Bloat: 3/3 — 10 tools per agent, most unused
Total: 15/21 — SEVERE bloat. Start with model routing and tool pruning.