reliable ai assistant builder
Design high-reliability AI assistants with strict evidence guardrails, authority levels, and escalation paths.
$5
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Example session with this skill installed
Design a boundary contract for a medical clinical trials assistant. It uses our internal protocol DB (Primary) and PubMed (Secondary). It should never give medical advice.
- Read your context and instructions
- Compiled the reliable ai assistant
I have designed the reliability framework for the Clinical Trials Assistant. This contract ensures the agent only cites internal protocols and peer-reviewed data while strictly enforcing the "no medical advice" boundary.
Clinical Trials Assistant: Boundary Contract
1. Approved Knowledge Sources
| Source ID | URI/Path | Type | Freshness | Confidence Weight |
|---|---|---|---|---|
| PROTO-DB | internal://clinical-protocols/v1 | Primary | Real-time | 1.0 |
| PUBMED-API | https://pubmed.ncbi.nlm.nih.gov | Secondary | Daily | 0.7 |
2. Evidence Taxonomy
| Classification | Meaning | Confidence Floor |
|---|---|---|
| Confirmed | Found in PROTO-DB | 0.9 |
| Inferred | Derived from PROTO-DB + PUBMED-API | 0.6 |
| Unconfirmed | Only present in PUBMED-API | 0.4 |
| Unknown | No match in approved sources | 0.0 |
3. Response Templates
[CONFIRMED] Protocol detail: <content>
Source: PROTO-DB
Confidence: 0.95
[BOUNDARY VIOLATION] I cannot provide medical advice or treatment recommendations.
Reason: Medical Advice Restriction (Safety Policy)
Escalation: Contact the Clinical Safety Board.
4. Escalation Routes
| Failure Mode | Escalation Target | SLA |
|---|---|---|
| Conflicting Protocols | Lead Trial Coordinator | 4 hours |
| Medical Advice Request | Chief Medical Officer (Log only) | Immediate |
| Source Connection Error | IT DevOps | 1 hour |
Next steps
- Review the confidence weights for PubMed sources.
- Integrate the Boundary Violation template into your system prompt.
- Run the adversarial test suite to ensure "medical advice" queries are blocked.
Connects securely to your tools. The creator never sees your data.
About this skill
The problem
LLMs frequently hallucinate policies, invent data when sources are missing, or take unauthorized actions because they lack clear operational boundaries. Without a formal evidence contract, agents become a liability in regulated or high-stakes environments.
What it does
- Generates a formal boundary contract defining approved knowledge sources and their confidence weights.
- Establishes an evidence taxonomy (Confirmed, Inferred, Unconfirmed, Unknown) with specific confidence floors.
- Defines a 4-tier authority hierarchy to manage system, domain, and user-level overrides.
- Builds structured response templates for handling missing, conflicting, or adversarial inputs.
- Codifies escalation routes and SLAs for human-in-the-loop intervention.
Why this beats prompting it yourself
Standard prompting relies on the model's "best effort" to be honest. This skill implements a systematic engineering framework that forces the model to categorize its own evidence before responding. It replaces vague "be helpful" instructions with a rigorous test-driven protocol that includes adversarial and ambiguity checks.
Use cases
- Deploying customer support agents for highly regulated insurance or legal policies.
- Building internal knowledge bots that must cite specific documentation and escalate when sources conflict.
- Creating enterprise AI "constitutions" to prevent prompt injection and unauthorized policy interpretations.
- Auditing existing agent configurations for reliability gaps and hallucination risks.
Known limitations
Does not handle UI copywriting or creative writing tasks. Requires an initial list of approved data sources to be effective.
Requires pre-defined data sources and domain knowledge. Not designed for creative or non-factual tasks. Freshness monitoring must be implemented externally.
How to use
- 1
Connect your agent to the Agensi MCP
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- 2
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- 3
Ask for what you need
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