guardrails
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Articles about guardrails
Safe Exploration: Guarding RL-Based Pricing in Digital Wallet Onboarding
Unsafe exploration is not an RL problem it is an architecture problem. By combining RAG-driven constraint retrieval, action masking, and deterministic guardrail nodes within a LangGraph workflow, we create a system where the RL agent can learn and optimize within a provably safe boundary. For digital wallet onboarding, this means better conversion rates without regulatory nightmares.
When Prompt Engineering Stops Helping: Model and Retrieval Changes in Enterprise Tax Management AI
Prompt engineering is a powerful tool, but it is not a panacea. In enterprise tax management, the cost of error is too high to rely on prompts alone. By building a multi-agent system with LangGraph, we can isolate failures: if retrieval is poor, we fix the vector store; if reasoning is weak, we upgrade the model; if compliance is risky, we add guardrails. This architectural approach ensures that our AI copilot is not just smart, but also safe, accurate, and auditable. The next step is to integrate real-time regulatory feeds and rigorous evaluation pipelines to evolve this POC into a production-grade platform.
The LLM Selection Challenge in Regulated Industries
The ReguCheck system demonstrated here provides both the infrastructure for production deployment and the instrumentation needed for rigorous model comparison. In regulated industries, trust is not given—it is engineered, measured, and continuously validated.
Beyond Prompt Engineering: When to Pivot to Architecture in Enterprise AI
Prompt engineering is the "first line of defense" in LLM applications. It is cost-effective and fast. However, in enterprise scenarios—especially in regulated industries like banking—there is a clear tipping point where tweaking prompts yields diminishing returns, and architectural changes become mandatory.
Prompt Design Lessons from Production Agent Failures: A Fintech Fixed Deposit Case Study
Learn 7 prompt design lessons from fintech production failures to build robust, compliant AI agents. Focus on structure, negative constraints, and auditability.
Designing Guardrails for LangGraph Agents in Enterprise Retail
Secure LangGraph agents in retail with multi-layered guardrails: input validation, execution supervision, and output compliance for autonomous workflows.
Agentic AI vs. Generative AI in 2025: What They Are and How to Choose
Learn what Agentic AI vs. Generative AI means in the 2025 business reality and how to choose the right approach with practical frameworks, governance steps, and ROI-focused rollout guidance.
