Tools: AI Agents in production: Why guardrails fail and what actually works

AI Agents in Production: Why Guardrails Fail and What Actually Works ## The Problem: Your Agent Stack Has a Blind Spot ## Why Current Solutions Don't Work ## 1. Observability = Post-Mortem ## 2. LLM Guardrails = Stateless Theater ## 3. RAG = Context Retrieval, Not Validation ## The Missing Layer: Decision Runtimes ## What Is a Decision Runtime? ## How It Works: The Technical Architecture ## Example: Preventing the $50K Hallucinated Refund ## Why Context Graph (ands Hypergraphs), not traditional databases? ## Real-World Use Cases ## 1. Fintech: Preventing Fraudulent Transactions ## 2. Healthcare: HIPAA-Compliant Agent Actions ## 3. SaaS: Customer-Facing Agents ## What We Built: Rippletide ## The Future: Decision Runtimes as Infrastructure Primitives ## Try It Yourself Monitoring is too late. Guardrails are too dumb. Here's the missing layer between your LLM and your database. Your AI agent just refunded $50,000 to the wrong customer. Your observability dashboard caught it... 3 minutes later. Your LLM guardrails? They checked for prompt injection and toxicity, but had no idea the customer ID was hallucinated. Your compliance team is asking questions you can't answer. This isn't a hypothetical. After building and selling three AI companies (Recast.AI to SAP, Ponicode to CircleCI), I've seen this pattern repeat: agents fail in production not because the LLM is bad, but because there's a missing layer in the stack. Here's the typical AI agent architecture today: Notice what's missing? There's no layer that validates decisions before execution. But nothing sits between "the LLM decided to do this" and "it's now done in production." Tools like Langfuse, LangSmith, and Arize are excellent for debugging after something breaks. But they're fundamentally reactive: They don't prevent bad decisions from executing. When your agent hallucinates a customer ID and processes a refund, observability tools take a perfect screenshot of the disaster. Guardrails (NeMo, Llama Guard, Anthropic's Constitutional AI) check for: This is critical for safety, but guardrails are stateless. They don't know: A guardrail will happily approve: "Process a refund of $50,000 for customer ID cust_hallucinated123" because the text itself looks fine. Vector databases help agents retrieve relevant context, but they don't enforce it. An agent can: RAG gives your agent a library card. It doesn't make sure they read the books. After our third company exit, we started working with AI teams at Series A startups and enterprise SaaS companies. The pattern was universal: agents needed a layer between decision and execution that could validate context, enforce policies, and audit actions in real-time. We call this a Decision Runtime. A decision runtime sits between your agent and your production systems. Before any action executes, it: Think of it like a type system for agent behavior. Your code won't compile if types don't match. Your agent won't execute if context doesn't validate. Here's the same stack with a decision runtime: The decision runtime maintains a context graph — a real-time representation of entities, relationships, and state that the agent must respect. Unlike RAG (which suggests context), the decision runtime enforces it. We built our decision runtime on hypergraph architecture because agent decisions aren't simple key-value lookups: Traditional approach: This is an n-ary relationship across multiple entities. Hypergraphs model this naturally: The entire decision context lives in a single traversable structure. No joins, no latency, no hallucination gaps. An AI agent processing payments needs to validate: A decision runtime blocks any transaction where context doesn't align, even if the LLM thinks it should proceed. Medical AI agents must audit every decision: Decision runtimes create immutable audit trails that satisfy regulatory requirements. Support agents powered by LLMs need boundaries: Without a decision runtime, you're hoping the LLM "remembers" your rules. After validating this with 30+ AI engineering teams, we built Rippletide — the first production context graph for AI agents. We're currently working with 8 design partners across fintech, healthtech, and AI-native SaaS companies. Five years ago, observability wasn't a "nice to have", it became infrastructure. Datadog, New Relic, and Sentry are now standard. Decision runtimes are following the same path. As agents move from demos to production, the gap between "the LLM decided" and "it executed in prod" becomes unacceptable. Regulated industries won't adopt agents without it. Enterprise buyers won't trust agents without it. The companies shipping reliable agents in 2026 will have three layers: If you're shipping AI agents to production and need to solve this problem: The best time to add a decision runtime is before your agent makes a $50K mistake. The second best time is now. Patrick Joubert is the founder and CEO of Rippletide. He previously founded and sold Recast.AI (acquired by SAP), Ponicode (acquired by CircleCI), and Beamap (acquired by Steria). Rippletide recently raised $5M in seed funding. Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse CODE_BLOCK: User Input ↓ Agent Framework (LangChain, CrewAI, etc.) ↓ LLM (GPT-4, Claude, etc.) ↓ Tools/APIs (Stripe, Database, Email, etc.) ↓ Production Systems Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: User Input ↓ Agent Framework (LangChain, CrewAI, etc.) ↓ LLM (GPT-4, Claude, etc.) ↓ Tools/APIs (Stripe, Database, Email, etc.) ↓ Production Systems CODE_BLOCK: User Input ↓ Agent Framework (LangChain, CrewAI, etc.) ↓ LLM (GPT-4, Claude, etc.) ↓ Tools/APIs (Stripe, Database, Email, etc.) ↓ Production Systems CODE_BLOCK: User Input ↓ Agent Framework ↓ LLM Output ↓ Decision Runtime ← [validates before execution] ↓ Tools/APIs (only if validated) ↓ Production Systems Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: User Input ↓ Agent Framework ↓ LLM Output ↓ Decision Runtime ← [validates before execution] ↓ Tools/APIs (only if validated) ↓ Production Systems CODE_BLOCK: User Input ↓ Agent Framework ↓ LLM Output ↓ Decision Runtime ← [validates before execution] ↓ Tools/APIs (only if validated) ↓ Production Systems CODE_BLOCK: // Agent wants to execute this const decision = { action: "process_refund", params: { customer_id: "cust_hallucinated123", amount: 50000, reason: "customer_request" } } // Decision runtime validates context const validation = await decisionRuntime.validate(decision) // Response: { valid: false, reason: "customer_id does not exist in context graph", blocked: true, alternative: "Request customer verification before refund" } // Agent receives feedback, can retry with correct context Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: // Agent wants to execute this const decision = { action: "process_refund", params: { customer_id: "cust_hallucinated123", amount: 50000, reason: "customer_request" } } // Decision runtime validates context const validation = await decisionRuntime.validate(decision) // Response: { valid: false, reason: "customer_id does not exist in context graph", blocked: true, alternative: "Request customer verification before refund" } // Agent receives feedback, can retry with correct context CODE_BLOCK: // Agent wants to execute this const decision = { action: "process_refund", params: { customer_id: "cust_hallucinated123", amount: 50000, reason: "customer_request" } } // Decision runtime validates context const validation = await decisionRuntime.validate(decision) // Response: { valid: false, reason: "customer_id does not exist in context graph", blocked: true, alternative: "Request customer verification before refund" } // Agent receives feedback, can retry with correct context CODE_BLOCK: Hyperedge: refund_decision_001 ├─ user: user_123 (role: support_agent) ├─ customer: cust_456 (status: verified) ├─ order: order_789 (amount: $50K, date: 2024-01-15) ├─ policy: refund_policy_v2 (max_amount: $10K) └─ audit: requires_manager_approval Validation: FAIL (amount exceeds policy limit) Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: Hyperedge: refund_decision_001 ├─ user: user_123 (role: support_agent) ├─ customer: cust_456 (status: verified) ├─ order: order_789 (amount: $50K, date: 2024-01-15) ├─ policy: refund_policy_v2 (max_amount: $10K) └─ audit: requires_manager_approval Validation: FAIL (amount exceeds policy limit) CODE_BLOCK: Hyperedge: refund_decision_001 ├─ user: user_123 (role: support_agent) ├─ customer: cust_456 (status: verified) ├─ order: order_789 (amount: $50K, date: 2024-01-15) ├─ policy: refund_policy_v2 (max_amount: $10K) └─ audit: requires_manager_approval Validation: FAIL (amount exceeds policy limit) - ✓ Query your database - ✓ Call your Stripe API - ✓ Send emails to customers - ✓ Make refunds - They log what happened - They alert you when metrics degrade - They help you understand failures - Prompt injection - Toxic output - PII leakage - Is this customer ID real? - Does this user have permission for this action? - Is this refund amount consistent with the order history? - Has this decision been audited for compliance? - Retrieve the correct customer data - Ignore it completely - Hallucinate different data - Execute anyway - Validates context — Are the entities in this decision real? - Enforces policies — Does this user have permission? Is this within limits? - Audits decisions — Creates an immutable record for compliance - Prevents hallucinations — Blocks actions based on non-existent data - "Does customer X exist?" → Database query - "Can user A refund customer B's order C, given policy D, audit trail E, and compliance requirement F?" - Customer identity - Transaction history - Regulatory compliance - Fraud patterns - Who accessed what patient data? - Was consent verified? - Is this action within protocol? - Can this agent offer a refund to this customer tier? - Is this discount within policy limits? - Does this user have permission for this account action? - Context graph store — Real-time entity and relationship tracking - Policy enforcement engine — Declarative rules for agent behavior - Audit-first architecture — Every decision is immutable and traceable - Framework-agnostic — Works with LangChain, CrewAI, raw LLM APIs - AWS Bedrock integration — Native support for Claude, Llama, etc. - Observability — What happened? - Guardrails — Is the output safe? - Decision Runtime — Should this execute? - Visit rippletide.com to learn more - We're accepting 2-3 more design partners for our beta program