Stop AI Agent Hallucinations with RAG & Evals

Why agents hallucinate in the first place

A language model, on its own, is a very good guesser. Ask it something and it will produce the most plausible-sounding continuation — whether or not it has the facts. When there is no grounding attached to the question, "plausible" and "correct" quietly come apart, and you get a confident, wrong answer. That is a hallucination, and in a production agent it is not a curiosity. It is a support ticket, a bad payment, or a compliance incident.

So the goal is not to make the model "smarter." It is to remove the situations where the model is allowed to guess. Every technique below is a way of narrowing what the agent is permitted to do until answering-from-nothing is no longer an option. This is applied production work, not theory — it draws on live systems, including a real-time voice agent you can actually pick up the phone and talk to.

The four disciplines that stop hallucination

Step 1

Ground answers in your real data with RAG

Retrieval-augmented generation retrieves the relevant passages from your corpus — docs, tickets, product data, policies — and puts them in front of the model before it answers. The model is now summarizing real, cited source material instead of recalling half-remembered training data. The single biggest reduction in hallucination comes from this one move: the agent answers from evidence, and when the evidence isn't there, it can say so instead of inventing. Retrieval quality has to be measured, not assumed — if retrieval surfaces the wrong passage, the model faithfully summarizes the wrong thing.

Read the deeper explainer: what is RAG?

Step 2

Constrain actions behind a model gateway

A tool-using agent that can call APIs, write records, or move money must never be improvising those calls. Route it through a model gateway: a single choke point where every tool call is defined, validated, permissioned, logged, and rate-limited, with graceful fallback when a provider degrades. The agent picks from a constrained menu of allowed actions with typed arguments — not free-form guessing at what to do next. This turns an opaque black box into something observable: you can see every action it took and why, and you can block the ones it shouldn't take.

Step 3

Prove reliability with an evaluation harness

You cannot fix what you cannot measure, and "it seemed better in the demo" is not measurement. An evaluation harness backtests the agent's output against historical ground truth — real past cases where you already know the correct answer — so a prompt change, a new model, or a retrieval tweak is proven to help before it reaches a customer. This is the difference between shipping on vibes and shipping on evidence, and it is what lets you change the system with confidence instead of fear.

Read the deeper explainer: what is an LLM evaluation harness?

Step 4

Sign and anchor every decision for traceability

When something does look wrong — and it eventually will — you need to answer one question fast: what context produced this answer? If every decision is signed and anchored to the exact inputs, retrieved passages, model version, and tool calls that created it, that becomes a lookup instead of a guessing game. It also makes the system independently auditable: you can prove after the fact what the agent saw and did, rather than asking anyone to take it on faith.

This is production work, not slideware

These four disciplines are not a reading list — they are how Hibiscus Consulting builds AI. Hibiscus Consulting LLC is the software studio of Blake Burnette, a founder-engineer in Cary, North Carolina who ships production AI end to end: model gateways routing tool-using agents, RAG pipelines, evaluation harnesses that backtest against historical ground truth, and real-time voice agents — including one you can call and talk to right now, a far harder bar than a text demo because latency and interruption handling are unforgiving.

$720M

payments run (neobank)

100%

uptime

2,500+

tests on the Nectar compiler

The positioning is the point: production AI whose every decision is signed, anchored, and independently auditable. That principle shows up as concrete, inspectable artifacts:

A tamper-evident audit trail signed with post-quantum cryptography (ML-DSA), so decisions are traceable and verifiable after the fact — a provisional patent has been filed.
Nectar, a programming language written in Rust that compiles to WebAssembly, with a public compiler backed by 2,500+ tests.
A multi-tenant payments and field-ops platform (Stripe, Temporal, Postgres, Rust/Node on GCP).

The operational track record behind that: roughly three years as Director of Engineering at a neobank, leading 17 engineers across 5 products and running $720M in payments at 100% uptime (Stripe Connect multi-tenant, Plaid ACH, Apple/Google Pay, fraud rules, KYC/KYB). Blake owned all commits on the Snap! Spend customer-facing React app and wrote core payments code across the platform. He also built a WebRTC platform for the Emmys in React in five weeks, and a React dashboard on a Rails EHR (Medaxion) — healthcare experience on top of nearly a decade of React and TypeScript.

Get your agent or RAG pipeline hardened

If your agent guesses when it should ground, calls tools it shouldn't, or ships changes you can't prove are safe — let's fix the pipeline, not just the prompt.

Email blake@hibiscus.buzz

Founding-engineer, senior/staff/director, or contract. Remote (US) or Triangle-local. SBIR-eligible small business.

Frequently asked questions

Why does my AI agent hallucinate?

Because it is answering without grounding. A language model on its own produces the most plausible-sounding continuation whether or not it has the facts, so when no real source material is attached to the question, "plausible" and "correct" come apart and you get a confident wrong answer. The fix is to stop letting the agent answer from nothing: ground it in your real data with RAG, and constrain what it's allowed to do.

Does RAG actually stop hallucinations?

RAG is the single biggest lever. Retrieval-augmented generation puts the relevant passages from your own corpus in front of the model before it answers, so it summarizes real, citable evidence instead of recalling training data, and it can say "I don't have that" when the evidence isn't there. The catch is that retrieval quality must be measured, not assumed — if retrieval surfaces the wrong passage, the model will faithfully summarize the wrong thing. So RAG plus evaluation, not RAG alone.

How do I keep a tool-using agent from taking wrong actions?

Put it behind a model gateway: a single choke point where every tool call is defined, validated, permissioned, logged, and rate-limited, with graceful fallback when a provider degrades. The agent then picks from a constrained menu of allowed actions with typed arguments rather than improvising API calls, which keeps its behavior both constrained and observable instead of a free-form black box.

How do I prove my agent is reliable before shipping a change?

Use an evaluation harness that backtests the agent's output against historical ground truth — real past cases where the correct answer is already known. That way a prompt change, a new model, or a retrieval tweak is proven to help before it reaches a customer, so you ship on evidence rather than on how the demo felt.

When an answer looks wrong, how do I find out what caused it?

Sign and anchor every decision to the exact inputs, retrieved passages, model version, and tool calls that produced it. Debugging then becomes a lookup instead of a guessing game, and the system is independently auditable — you can prove after the fact what the agent saw and did rather than asking anyone to take it on faith.