ValGuard.ai

A review queue that clears after the message went out is not a control. For AI drafts in a patient portal, the check runs before send: no medication directives without provider contact, a disclaimer when the text talks about treatment, no record numbers on a phone screen.

Green uptime, bad outputs. An agent can run for weeks while more of its answers fail validation. Nothing crashes, so nobody gets paged. A fixed threshold catches spikes, not slow drift. Compare each agent with its own last week and with its peers.

A retry on 429 does not fix a bad answer that came back with HTTP 200. Those are two different failures. One is the provider. The other is the output. Fallback that only watches status codes will call a wrong answer a success.

An AI patch can do exactly what you asked and still turn off TLS verification three lines down. The reviewer checks the retry logic, it works, PR approved. A prompt rule won't stop that. A check on the diff that runs every time and names the line will.

A finance chatbot doesn't have to move money to cause trouble. "This fund has delivered 12% annually" in a chat window reads like a promise. The reply is 200 OK, dashboards stay green. The problem is the words, and only a check on every draft catches them.

Constrained decoding means the model can only emit JSON that fits your schema. Now the schema is the weak point. If the ERP dropped an enum value last month and nobody updated the schema, the model can still return the old value, perfectly formatted. Check against the live contract before you POST.

Ask your team what "0.7" meant in your LLM judge rubric last March. If nobody knows, that score should not approve a refund on its own. Let the judge score tone. Let code check totals, enums and limits before any tool runs.

We ran job-post variants through our own hiring-language guard and published what got through. "Native English speaker required" is blocked. "Native English speaker only" passes. "Recent grads only" passes. A pattern list blocks what you configured, nothing more. Tune it in shadow mode first.

Two weak answers to "prove your AI guardrails ran": keep every transcript forever (privacy risk, editable) or point at a policy doc (proves nothing). Better: per request, record which rules ran, what they decided, a config hash and payload digests. Sign it.

Quick test for any AI agent in production: pick one business rule from your system prompt (a refund cap, a discount limit) and ask how you'd know it was broken yesterday. If the answer is "a customer would tell us", that rule isn't enforced. It's hoped for.

Schema validation answers "is this well-formed?" It does not answer "is this the right refund, routing, or CRM write?" Agents need both layers on every step, not a hope that the next model call will notice.

Cheap-first pattern for LLM agents: run the cheaper model first, validate the output with deterministic rules, and escalate to the expensive model only when a check fails. Most requests never need the big model.

Multi-agent systems break quietly: bad handoffs, corrupted state, runaway loops. Self-healing isn’t “let the model try again.” It’s deterministic repair in microseconds — before validators run. Three tiers of response repair. valguard.ai/blog/self-he...

Guardrails AI = Python validators next to your code. ValGuard = shared policy proxy + rule IDs + playbooks that block bad handoffs before the next agent runs. Same rules for every language. Shadow → enforce without redeploys. valguard.ai/vs/guardrail... #AI #LLMValidation #Agents

The dangerous AI insurance failure isn't always a hallucination. Sometimes it's a fluent sentence like: “Your loss is covered.” Our FNOL example shows how deterministic controls can stop AI from making promises it isn't authorized to make. valguard.ai/blog/insuran... #AI #InsurTech

Most multi-agent systems don’t fail with a bang. They fail with a quiet cascade: bad handoff → corrupted state → downstream agents inherit the mess. Real self-healing is deterministic recovery, not another model roll of the dice. New post: valguard.ai/blog/self-he... #AIAgents #ProductionAI #LLM

ValGuard vs n8n, Portkey & Guardrails AI. They look similar. They solve different jobs. n8n → connect tools Portkey → manage keys & spend Guardrails AI → checks in one service ValGuard → block bad output before the next step Who each is for: valguard.ai/blog/valguar... #AI #LLM #Guardrails

Most AI agents look great until the day they quietly ship a wrong amount or a policy violation - all with HTTP 200. The missing layer is deterministic orchestration: generation, flow control, and rule enforcement as separate jobs, with validation on every step. valguard.ai/blog/determi...

"It works most of the time" is how quiet AI failures start. 99.7% at 10k requests/day = ~30 silent failures. Most return 200 OK. That’s why we built ValGuard. valguard.ai/blog/why-we-... #AI #LLM #AIAgents