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02 · Research · Agent Architecture
AI Agent Architecture Audit
Diagnoses agent-system failures across prompts, memory, tools, wrappers, and output delivery.
---
name: agent-architecture-audit
description: Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.
metadata:
origin: oh-my-agent-check
tools: Read, Write, Edit, Bash, Grep, Glob
---
# Agent Architecture Audit
A diagnostic workflow for agent systems that hide failures behind wrapper layers, stale memory, retry loops, or transport/rendering mutations.
## When to Activate
**MANDATORY for:**
- Releasing any agent or LLM-powered application to production
- Shipping features with tool calling, memory, or multi-step workflows
- Agent behavior degrades after adding wrapper layers
- User reports "the agent is getting worse" or "tools are flaky"
- Same model works in playground but breaks inside your wrapper
- Debugging agent behavior for more than 15 minutes without finding root cause
**Especially critical when:**
- You've added new prompt layers, tool definitions, or memory systems
- Different agents in your system behave inconsistently
- The model was fine yesterday but is hallucinating today
- You suspect hidden repair/retry loops silently mutating responses
**Do not use for:**
- General code debugging — use `agent-introspection-debugging`
- Code review — use language-specific reviewer agents
- Security scanning — use `security-review` or `security-review/scan`
- Agent performance benchmarking — use `agent-eval`
- Writing new features — use the appropriate workflow skill
## The 12-Layer Stack
Every agent system has these layers. Any of them can corrupt the answer:
| # | Layer | What Goes Wrong |
|---|-------|----------------|
| 1 | System prompt | Conflicting instructions, instruction bloat |
| 2 | Session history | Stale context injection from previous turns |
| 3 | Long-term memory | Pollution across sessions, old topics in new conversations |
| 4 | Distillation | Compressed artifacts re-entering as pseudo-facts |
| 5 | Active recall | Redundant re-summary layers wasting context |
| 6 | Tool selection | Wrong tool routing, model skips required tools |
| 7 | Tool execution | Hallucinated execution — claims to call but doesn't |
| 8 | Tool interpretation | Misread or ignored tool output |
| 9 | Answer shaping | Format corruption in final response |
| 10 | Platform rendering | Transport-layer mutation (UI, API, CLI mutates valid answers) |
| 11 | Hidden repair loops | Silent fallback/retry agents running second LLM pass |
| 12 | Persistence | Expired state or cached artifacts reused as live evidence |
## Common Failure Patterns
### 1. Wrapper Regression
The base model produces correct answers, but the wrapper layers make it worse.
**Symptoms:**
- Model works fine in playground or direct API call, breaks in your agent
- Added a new prompt layer, existing behavior degraded
- Agent sounds confident but is confidently wrong
- "It was working before the last update"
### 2. Memory Contamination
Old topics leak into new conversations through history, memory retrieval, or distillation.
**Symptoms:**
- Agent brings up unrelated past topics
- User corrections don't stick (old memory overwrites new)
- Same-session artifacts re-enter as pseudo-facts
- Memory grows without bound, degrading response quality over time
### 3. Tool Discipline Failure
Tools are declared in the prompt but not enforced in code. The model skips them or hallucinates execution.
**Symptoms:**
- "Must use tool X" in prompt, but model answers without calling it
- Tool results look correct but were never actually executed
- Different tools fight over the same responsibility
- Model uses tool when it shouldn't, or skips it when it must
### 4. Rendering/Transport Corruption
The agent's internal answer is correct, but the platform layer mutates it during delivery.
**Symptoms:**
- Logs show correct answer, user sees broken output
- Markdown rendering, JSON parsing, or streaming fragments corrupt valid responses
- Hidden fallback agent quietly replaces the answer before delivery
- Output differs between terminal and UI
### 5. Hidden Agent Layers
Silent repair, retry, summarization, or recall agents run without explicit contracts.
**Symptoms:**
- Output changes between internal generation and user delivery
- "Auto-fix" loops run a second LLM pass the user doesn't know about
- Multiple agents modify the same output without coordination
- Answers get "smoothed" or "corrected" by invisible layers
## Audit Workflow
### Phase 1: Scope
Define what you're auditing:
- **Target system** — what agent application?
- **Entrypoints** — how do users interact with it?
- **Model stack** — which LLM(s) and providers?
- **Symptoms** — what does the user report?
- **Time window** — when did it start?
- **Layers to audit** — which of the 12 layers apply?
### Phase 2: Evidence Collection
Gather evidence from the codebase:
- **Source code** — agent loop, tool router, memory admission, prompt assembly
- **Logs** — historical session traces, tool call records
- **Config** — prompt templates, tool schemas, provider settings
- **Memory files** — SOPs, knowledge bases, session archives
Use `rg` to search for anti-patterns:
```bash
# Tool requirements expressed only in prompt text (not code)
rg "must.*tool|必须.*工具|required.*call" --type md
# Tool execution without validation
rg "tool_call|toolCall|tool_use" --type py --type ts
# Hidden LLM calls outside main agent loop
rg "completion|chat\.create|messages\.create|llm\.invoke"
# Memory admission without user-correction priority
rg "memory.*admit|long.*term.*update|persist.*memory" --type py --type ts
# Fallback loops that run additional LLM calls
rg "fallback|retry.*llm|repair.*prompt|re-?prompt" --type py --type ts
# Silent output mutation
rg "mutate|rewrite.*response|transform.*output|shap" --type py --type ts
```
### Phase 3: Failure Mapping
For each finding, document:
- **Symptom** — what the user sees
- **Mechanism** — how the wrapper causes it
- **Source layer** — which of the 12 layers
- **Root cause** — the deepest cause
- **Evidence** — file:line or log:row reference
- **Confidence** — 0.0 to 1.0
### Phase 4: Fix Strategy
Default fix order (code-first, not prompt-first):
1. **Code-gate tool requirements** — enforce in code, not just prompt text
2. **Remove or narrow hidden repair agents** — make fallback explicit with contracts
3. **Reduce context duplication** — same info through prompt + history + memory + distillation
4. **Tighten memory admission** — user corrections > agent assertions
5. **Tighten distillation triggers** — don't compress what shouldn't be compressed
6. **Reduce rendering mutation** — pass-through, don't transform
7. **Convert to typed JSON envelopes** — structured internal flow, not freeform prose
## Severity Model
| Level | Meaning | Action |
|-------|---------|--------|
| `critical` | Agent can confidently produce wrong operational behavior | Fix before next release |
| `high` | Agent frequently degrades correctness or stability | Fix this sprint |
| `medium` | Correctness usually survives but output is fragile or wasteful | Plan for next cycle |
| `low` | Mostly cosmetic or maintainability issues | Backlog |
## Output Format
Present findings to the user in this order:
1. **Severity-ranked findings** (most critical first)
2. **Architecture diagnosis** (which layer corrupted what, and why)
3. **Ordered fix plan** (code-first, not prompt-first)
Do not lead with compliments or summaries. If the system is broken, say so directly.
## Quick Diagnostic Questions
When auditing an agent system, answer these:
| # | Question | If Yes → |
|---|----------|----------|
| 1 | Can the model skip a required tool and still answer? | Tool not code-gated |
| 2 | Does old conversation content appear in new turns? | Memory contamination |
| 3 | Is the same info in system prompt AND memory AND history? | Context duplication |
| 4 | Does the platform run a second LLM pass before delivery? | Hidden repair loop |
| 5 | Does the output differ between internal generation and user delivery? | Rendering corruption |
| 6 | Are "must use tool X" rules only in prompt text? | Tool discipline failure |
| 7 | Can the agent's own monologue become persistent memory? | Memory poisoning |
## Anti-Patterns to Avoid
- Avoid blaming the model before falsifying wrapper-layer regressions.
- Avoid blaming memory without showing the contamination path.
- Do not let a clean current state erase a dirty historical incident.
- Do not treat markdown prose as a trustworthy internal protocol.
- Do not accept "must use tool" in prompt text when code never enforces it.
- Keep findings direct, evidence-backed, and severity-ranked.
## Report Schema
Audits should produce structured reports following this shape:
```json
{
"schema_version": "ecc.agent-architecture-audit.report.v1",
"executive_verdict": {
"overall_health": "high_risk",
"primary_failure_mode": "string",
"most_urgent_fix": "string"
},
"scope": {
"target_name": "string",
"model_stack": ["string"],
"layers_to_audit": ["string"]
},
"findings": [
{
"severity": "critical|high|medium|low",
"title": "string",
"mechanism": "string",
"source_layer": "string",
"root_cause": "string",
"evidence_refs": ["file:line"],
"confidence": 0.0,
"recommended_fix": "string"
}
],
"ordered_fix_plan": [
{ "order": 1, "goal": "string", "why_now": "string", "expected_effect": "string" }
]
}
```
## Related Skills
- `agent-introspection-debugging` — Debug agent runtime failures (loops, timeouts, state errors)
- `agent-eval` — Benchmark agent performance head-to-head
- `security-review` — Security audit for code and configuration
- `autonomous-agent-harness` — Set up autonomous agent operations
- `agent-harness-construction` — Build agent harnesses from scratch
When to use
Use when an agent or LLM feature behaves inconsistently, tool use is unreliable, or a system needs a pre-release architecture review. It is not a general code-debugging or security-scanning workflow.
What you get
• Severity-ranked findings • Architecture diagnosis • Ordered fix plan
How it works
1. Define the system, symptoms, and audit scope. 2. Collect code, configuration, logs, and memory evidence. 3. Map failure causes and propose code-first fixes.
Requirements
Requires access to the relevant application code, logs, configuration, and memory artifacts; shell and search tools such as `rg` are used in the examples. No separate reference files are specified. Optional upstream related skills are not included in this catalog: agent-introspection-debugging, agent-eval, security-review, autonomous-agent-harness, agent-harness-construction. M11 relations are curated follow-up guidance, not equivalent replacements. Do not claim these external skills have been loaded.
Starting prompt
Use the skill below to investigate [AGENT FAILURE]. Ask for relevant code, logs and configuration. Separate evidence from hypotheses and return an ordered fix plan before making changes.
Not for
A full security certification or a substitute for evidence from code and runtime logs.
What M11 added
M11 added diagnostic routing, evidence requirements, clear exclusions and stable packaging around the unchanged agent-architecture audit.
MIT License
Copyright (c) 2026 Affaan Mustafa
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Related skills & prerequisites
ai-context-window-audit · together — Inspect context overhead when diagnosing architecture failures.
multi-agent-orchestration · next — Translate the diagnosis into bounded agent ownership and handoffs.