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Grok Bot+18 agentsAgent Harness Architect Skill
Design and audit the system harness around AI models to ensure reliable tool-use, safety, and measurable performance.
$23.99
Agent Harness Architect Skill
Example session with this skill installed
The customer support agent is hallucinating tool arguments and retrying failed API calls until it hits rate limits. I need a harness audit and a resilience plan.
- Read your context and instructions
- Compiled the agent harness architect
I have analyzed the reported failure modes. The primary issues are a lack of input validation before tool execution and an unbounded retry policy on permanent 4XX errors.
Agent Harness Audit: Support Bot Resilience
| Component | Current State | Proposed Change | Impact |
|---|---|---|---|
| Tool Interface | Loose JSON schema; no pre-flight checks | Strict Pydantic validation; argument type-casting | Prevents malformed API calls |
| Control Flow | Infinite retry loop on any error | Bounded retries (n=3) with exponential backoff | Prevents rate-limit exhaustion |
| Validation | Post-hoc success check only | Semantic verification of tool output against goal | Reduces groundedness errors |
| Resilience | Blind retries for 400 & 429 errors | Circuit breaker for 429s; immediate escalation for 400s | Preserves token budget |
Proposed Architecture Changes
Implement Schema Guardrails: Wrap the call_support_api tool in a validator that rejects arguments missing the mandatory customer_id UUID format.
2.
Deterministic Routing: Move the "retry vs. escalate" logic out of the model and into a deterministic wrapper based on HTTP status codes.
3.
Observation Filtering: Truncate raw API error traces before they are fed back into the model to prevent "error looping" where the agent tries to fix a system error via chat.
Next steps
- Deploy a Pydantic schema for the
refund_processortool to block hallucinated currency codes. - Configure a circuit breaker with a 30-second cooldown for the main support endpoint.
- Run a 50-trace pairwise evaluation to ensure the new validation layer doesn't increase latency beyond 200ms.
Connects securely to your tools. The creator never sees your data.
About this skill
Your AI agent is failing, but the model might not be the problem. Most production issues stem from poor harness design: weak context assembly, lack of validation, or brittle control flow. This skill provides a systematic engineering framework to audit and optimize the entire runtime environment surrounding your LLM.
What it does
- Architectural audits identifies bottlenecks in instruction stacks, memory policies, and routing logic.
- Diagnostic mapping traces the request path from input through assembly, model execution, and validation.
- Tool reliability design improves tool-call schemas, argument validation, and idempotent error handling.
- Evaluation planning creates controlled experiments with stable baselines to measure quality, latency, and cost.
- Safety engineering implements least privilege, action allowlists, and human-in-the-loop approval gates.
How it works
- Define the failure by converting vague complaints into a structured failure taxonomy with measurable symptoms.
- Map the harness to visualize trust boundaries, state transitions, and side effects in the current request path.
- Rank hypotheses to isolate the most likely causes based on evidence, then design discriminating tests.
- Execute controlled experiments changing one factor at a time while tracking regressions and tail latency.
Frameworks & tools
Works with any agentic architecture including LangChain, LlamaIndex, or custom Python/TypeScript runtimes. Applicable to OpenAI, Anthropic, and local model deployments.
Why this beats prompting it yourself
Generic prompting ignores the system-level interactions that cause production failures. This skill enforces rigorous engineering principles like circuit breakers, source provenance, and bounded retries that simple "system instructions" often overlook.
Use cases
- Stabilizing inconsistent tool-use in production customer support agents.
- Reducing token overhead and latency in complex RAG pipelines.
- Auditing autonomous agents for security and unauthorized side effects.
- Benchmarking new model versions against existing harness configurations.
Known limitations
Does not provide formal proof of correctness or fix inherent model knowledge gaps. Requires access to traces or logs to perform high-confidence diagnostics.
Requires existing traces or logs for accurate diagnostics. Cannot fix hardware-level latency or fundamental model reasoning gaps. Does not provide executable code for every possible framework.
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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