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Grok Bot+18 agentsAI Agent Analysis & Improvement Advisor
A senior agent quality engineer skill for auditing, debugging, and optimizing AI agent architectures and workflows. ( Analysis + Advisor + Improvement Plan + Validation )
$38.99
AI Agent Analysis & Improvement Advisor
Example session with this skill installed
Audit my customer support agent. It keeps hallucinating order statuses when the 'get_order' tool returns a 404. Here are the prompt, tool definition, and two failure logs.
- Read your context and instructions
- Compiled the ai agent analysis
I have analyzed the provided prompt, tool definition, and failure logs. The hallucination stems from a lack of explicit grounding instructions for null tool returns.
Agent Audit Report
1. Executive Diagnosis
The agent defaults to creative generation when the get_order tool returns a 404 Not Found error. The instruction hierarchy lacks a "Negative Constraint" section, causing the LLM to fill data gaps with training data patterns.
2. Audit Scorecard
| Area | Status | Evidence |
|---|---|---|
| Grounding | Needs Work | Observed: Agent invented order #8821 in Log B. |
| Tool Selection | Pass | Observed: get_order called with correct UUID format. |
| Error Handling | Needs Work | Observed: 404 status treated as successful empty return. |
| Instruction Hierarchy | Needs Work | Inferred: Priority given to "Helpful Assistant" over "Factual Accuracy". |
3. Root-Cause Hypothesis
- Symptom: Hallucinated order delivery dates.
Likely Cause: The tool return is passed as a raw string {"error": "not found"} which the agent interprets as a prompt to find the info elsewhere.
- Confidence: High.
4. Implementation Design: Improved Grounding
Target Behavior: Agent must state "Order not found" and offer to check the email address if the tool returns a 404.
*
Exact Change: Add to System Prompt: If a tool returns a 404 error, you MUST NOT invent data. Explicitly state the information is missing.
- Acceptance Criteria: Zero hallucinated dates across 10 test runs with non-existent IDs.
Next steps
- Update the system prompt with the provided negative constraints.
- Modify the tool output wrapper to include a
status_codefield for clearer agent routing. - Run the adversarial test cases provided in the validation plan.
Connects securely to your tools. The creator never sees your data.
About this skill
Technical debt in AI agents leads to unpredictable hallucinations, tool-use failures, and spiraling inference costs. This skill acts as a senior agent quality engineer to audit, debug, and optimize your agent architectures. It moves beyond generic advice to provide a verifiable improvement plan based on system logs, prompt hierarchies, and RAG performance.
What it does
- Architectural Audit evaluating task decomposition, routing logic, and state management.
- Root-Cause Analysis identifying why agents fail using a symptom-to-evidence mapping system.
- Reliability Engineering reviewing error handling, retry logic, and tool argument correctness.
- Security & Safety Review checking for prompt injection vulnerabilities and untrusted data handling.
- Improvement Backlog scoring proposed changes by impact, confidence, effort, and risk.
- Validation Planning designing happy-path, edge-case, and adversarial regression tests.
How it works
- Intake where you provide agent instructions, tool definitions, sample failure logs, and success criteria.
- Analysis where the skill builds an evidence map and scorecards the system across 15+ dimensions.
- Hypothesis generation to isolate specific causes for hallucinations or tool selection errors.
- Implementation design including target behavior, exact changes, and rollback paths.
Frameworks & tools
Works with any LLM provider (OpenAI, Anthropic, Google) and agent framework including LangChain, CrewAI, AutoGPT, or custom MCP-based implementations.
Why this beats prompting it yourself
Generic prompts often suggest adding more instructions, which increases context drift. This skill uses a structured engineering framework to remove complexity, prioritize measurable fixes, and ensure improvements are backed by a formal validation plan.
Use cases
- Debugging an agent that consistently chooses the wrong tools for specific API calls.
- Reducing latency and cost by optimizing instruction hierarchy and task decomposition.
- Hardening production agents against prompt injection and data leakage.
- Establishing a regression testing suite before deploying major agent updates.
Known limitations
Does not perform live code execution or direct API deployments. Requires manual provision of logs and prompt files to generate findings.
Requires manual provision of logs and prompt files. Does not execute code or access live production environments directly. Confined to analysis and planning.
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
Sign in to Agensi
Your agent shows a sign-in link. Sign in and click Allow.
- 3
Ask for what you need
Your agent loads this skill and uses it on your task. It always asks before any payment.
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