Four ways AI breaks.One reliability stack.
AI doesn’t break in one place — it breaks in four: bad retrieval, prompt and model drift, injection attacks, and agent misbehavior. The AI Reliability Bundle is one CI-ready tool for each, plus a playbook that wires them into a single eval gate. Ship on a verdict, not a hope.
“Our AI works” is not a test result.
AI systems fail in four distinct places, and each is invisible to the others’ tests. The retrieval is irrelevant. A prompt tweak quietly degrades cases that used to pass. A crafted input overrides your instructions. An agent loops, calls the wrong tool, or blows its budget — and still returns a “correct” answer.
One tool can’t see all four. A passing retrieval score says nothing about injection resistance; a green prompt suite says nothing about an agent that looped five times. This bundle is one CI-ready tool for each failure mode, built to run as a single reliability gate.
Four tools. One reliability stack.
Each tool is the same full product sold on its own page — nothing trimmed for the bundle. Click any card to see the individual product page.
RAG Retrieval Grader
Score whether the context you feed the model is relevant and complete — Recall@k, Precision@k, MRR, nDCG against a gold set — and fail CI when retrieval drops. The root cause of confident hallucinations, measured.
Bad retrieval — the model answers from irrelevant or missing context.
View the full toolPrompt Regression Lab
Snapshot a baseline, diff every prompt or model change, and fail the build on any regression — deterministic checks, A/B compare, a ship / hold / regressed verdict in Python and TypeScript.
Prompt / model drift — a tweak quietly degrades cases that used to pass.
View the full toolPrompt Injection Red Team Kit
Run 15 OWASP-mapped injection and system-prompt-leak probes against your own app, score severity-weighted resilience, and gate CI on a verdict. Defensive, authorized-use only.
Injection / jailbreak — a crafted input overrides instructions or leaks data.
View the full toolAgent Reliability Harness
Evaluate the whole agent trajectory — tool choice, argument validity, step efficiency, cost, and policy — and gate CI on a SHIP / HOLD / FIX verdict. Six deterministic evaluators, framework-agnostic.
Agent misbehavior — loops, wrong tool calls, hallucinated args, blown budgets.
View the full toolIncluded: the CI-Pipeline Playbook
A connective guide that wires all four tools into one make eval / CI gate — shared baselines, per-tool thresholds, and a single combined reliability report. Authored for the bundle, not for any one tool.
One eval pipeline, gated in CI.
The four tools share the same shape — test cases in, a score and a pass/fail verdict out — so they fold into a single reliability gate.
make eval -> rag-grade # retrieval quality >= threshold
prompt-regress # no regressions vs baseline
redteam # injection catch-rate >= threshold
agent-eval # trajectory verdict != FIX
-> one reliability report -> gate the buildOne make eval gate, four lenses
The tools share the same shape — test cases in, a score and a pass/fail verdict out — so they fold into a single CI job: rag-grade, prompt-regress, redteam, agent-eval, then one combined reliability report that gates the build. The included playbook wires it.
Each blind spot is invisible to the others
A clean retrieval score says nothing about injection resistance. A correct final answer hides an agent that looped five times. A passing prompt suite says nothing about a jailbreak. You need all four lenses because no one of them can see the others' failures.
Deterministic, so it runs on every PR
All four favor deterministic, rule-based checks by default — reproducible and cheap enough to gate every pull request, with optional LLM-judge steps where useful. Shared baselines mean reliability stops being a vibe and becomes a check.
One spine runs through all four.
Every tool in the stack carries the same posture: tell the truth about quality before you ship, and refuse to bless work that isn’t actually safe. Each one ships that posture as a runnable, CI-gating verdict.
These are offline / CI evaluation tools, not runtime guardrails — they don't sit in the live request path. Catch problems before you ship, then pair with runtime guardrails in production for defense in depth.
Rule-based checks that are reproducible and cheap, with no required LLM-judge bill — add a judge only where subjective grading earns it. The verdict you get today is the verdict you get tomorrow on the same inputs.
Default thresholds and policies are sensible starting points, not a universal standard. Each tool grades what you tell it to — the quality of the gate is the quality of the cases and budgets you write.
Built for teams shipping LLMs and agents.
AI/ML engineers shipping to prod
A real reliability gate across retrieval, prompts, security, and agents — one verdict you can put in front of a release instead of a hope.
Platform & infra teams
Reproducible, cheap evals you can standardize across services and CI, with shared baselines and one combined report rather than four disconnected toolchains.
Startups & agencies building agents
Cover the whole failure surface without assembling four toolchains yourself — and leave clients with a repeatable reliability stack instead of a spreadsheet of vibes.
One purchase. Four tools. Save $97.
- RAG Retrieval Grader$89
- Prompt Regression Lab$89
- Prompt Injection Red Team Kit$99
- Agent Reliability Harness$149
- CI-Pipeline Playbookincluded
- Bought separately$426
Roughly the Agent Reliability Harness for free when you take the whole stack. Each tool also stands alone; see all RedHub bundles.
Answers before you ask.
Four CI-ready LLM dev tools, sold together: the RAG Retrieval Grader ($89), the Prompt Regression Lab ($89), the Prompt Injection Red Team Kit ($99), and the Agent Reliability Harness ($149) — plus a connective CI-pipeline playbook that wires them into one eval gate with shared baselines and a single reliability report. Bought separately they total $426; the bundle is $329, so you save $97.
They cover the four distinct ways LLM and agent systems break in production: bad retrieval, prompt/model drift, injection attacks, and agent misbehavior. Each is invisible to the others' tests — a passing retrieval score says nothing about injection resistance, and a correct final answer hides an agent that looped. You need all four lenses, and they're built to share the same baseline-and-verdict shape so they run as one gate.
Yes — identical contents, lower price. The bundle is the four full products exactly as sold on their own pages, nothing trimmed, plus the connective playbook. You're paying $329 instead of $426 for the same files, and each tool is also sold standalone.
Yes to CI — each gates the build, and they're designed to share a baseline-and-verdict shape so they run as a single reliability job. They favor deterministic, rule-based checks by default, so they're reproducible and cheap enough to run on every PR; optional LLM-judge steps are available where subjective grading helps, but never required.
No — they're offline/CI evaluation tools, not live request-path guardrails. Use them to catch problems before you ship, and pair them with runtime guardrails in production for defense in depth. The Red Team Kit in particular is for defensive, authorized testing of your own application only.
They're developer-first and framework-agnostic — you bring test cases and traces, so they work with common stacks (LangGraph, CrewAI, OpenAI, your own logs) via thin adapters. The Regression Lab and the Red Team Kit ship matched Python and TypeScript engines; the RAG Grader ships both too; the Agent Reliability Harness is Python with zero required dependencies. See each product page for specifics.
Buy the bundle if two or more of the four still fit how you work — otherwise the math may not favor it. We don't currently offer partial credit for an already-owned tool; the bundle's discount is calibrated to the set as a whole. Each tool stays available standalone.
Cover the whole
failure surface.
Four CI-ready tools, one eval pipeline, one verdict you can trust — $329 instead of $426. Deterministic, offline, gate-your-build. Thirty-day refund either way.
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