Daily recap for June 14, 2026

Seven production-grade AI recipes landed today, each a ready-to-download tutorial that solves a concrete business problem — from turning phone calls into auto-repair estimates to enforcing LLM spend caps on Stripe billing. Pick the one that matches your workflow and try it this afternoon.

New tutorials

vLLM Agent Quality Gate for On-Prem SMB Support Bots

When you run support agents on your own vLLM hardware, there’s no systematic way to catch regressions after a model update or prompt change. This recipe builds a CI-friendly quality gate that runs against your vLLM endpoint, compares agent responses to golden conversation logs, and blocks deployment if quality drops below your threshold. It ships batteries‑included: pre‑baked evaluation scenarios, metric export to Langfuse, and an HTTP endpoint your pipeline can call.

Read the tutorial → · Download the code (zip)

Built with @reaatech/agent-eval-harness-cli, @reaatech/agent-eval-harness-gate, @reaatech/agent-eval-harness-trajectory, @reaatech/agent-eval-harness-golden, on vllm · 64 tests · 100.00% coverage.

Mistral AI Spend Governance for Stripe SMB Subscription Billing

Small businesses running multiple AI agents on Stripe billing workflows need cost controls to prevent runaway API spends. This tutorial wires up an Express server and a Next.js dashboard to enforce per‑customer LLM spend caps. When a tenant nears their soft cap, the router automatically downgrades to a cheaper Mistral model; at the hard cap, all calls are blocked until the cycle resets.

Read the tutorial → · Download the code (zip)

Built with @reaatech/agent-budget-engine, @reaatech/llm-router-engine, @reaatech/llm-cost-telemetry, @reaatech/agent-budget-spend-tracker, @reaatech/agent-budget-pricing, on mistral · 98 tests · 98.85% coverage.

Perplexity PII Shield for SMB E-commerce Support Chat

E‑commerce support teams often paste chat transcripts with credit card numbers and addresses into Perplexity for reply drafts — a compliance risk. This recipe places a guardrail pipeline in front of the Perplexity API that strips PII and blocks prompt injection before the text ever leaves your infrastructure. Redactions are logged, and the sanitized prompt still produces context‑aware replies.

Read the tutorial → · Download the code (zip)

Built with @reaatech/guardrail-chain, @reaatech/guardrail-chain-guardrails, @reaatech/guardrail-chain-config, @reaatech/pi-bench-core, @reaatech/guardrail-chain-observability, on perplexity · 61 tests · 98.61% coverage.

Voice quote assistant for auto-repair shops

Answering “how much to fix my brakes?” without interrupting the service bay is a tire‑shop owner’s daily frustration. This tutorial builds a voice agent that answers Twilio calls, transcribes speech, looks up parts and pricing, and speaks a structured estimate back to the customer. It uses Deepgram for STT/TTS, the OpenAI Responses API, and REAA’s voice‑agent and caching packages, with Langfuse tracing for visibility.

Read the tutorial → · Download the code (zip)

Built with @reaatech/voice-agent-core, @reaatech/voice-agent-stt, @reaatech/voice-agent-tts, @reaatech/voice-agent-telephony, @reaatech/agent-memory, @reaatech/llm-cache, on openai · 114 tests · 99.18% coverage.

Automated review response agent for tire shops

A one‑star Google review festers while the shop owner turns wrenches. This recipe ingests reviews from Google, Yelp, or Facebook, filters them through a guardrail chain (PII redaction, toxicity, sentiment), and generates empathetic, work‑order‑aware replies using the best LLM model for the cost. A dashboard lets you approve responses before they go live.

Read the tutorial → · Download the code (zip)

Built with @reaatech/agent-memory-core, @reaatech/agent-memory-retrieval, @reaatech/llm-router-core, @reaatech/llm-router-strategies, @reaatech/guardrail-chain, @reaatech/guardrail-chain-guardrails, on agnostic · 91 tests · 96.03% coverage.

Anthropic Lead Intake for Pipedrive SMB Lead Enrichment

Lean sales teams lose high‑intent Pipedrive leads to manual research delays. This pipeline ingests Pipedrive webhooks, enriches the lead with Anthropic (company data and fit scores), routes based on confidence, and updates the deal — all within a single request, with spend limits and Langfuse traces built in.

Read the tutorial → · Download the code (zip)

Built with @reaatech/agent-mesh, @reaatech/webhook-relay-core, @reaatech/confidence-router, @reaatech/agent-budget-engine, @reaatech/llm-cost-telemetry, @reaatech/session-continuity, on anthropic · 99 tests · 100.00% coverage.

Mistral AI Lead Intake for Marketo Lead Qualification & Routing

Marketo leads often go unworked in small teams because scoring and routing aren’t instant. This solution ingests Marketo webhooks, enriches leads with Mistral, routes them by confidence, and preserves conversation state for follow‑ups. Budget enforcement and response caching keep costs predictable.

Read the tutorial → · Download the code (zip)

Built with @reaatech/agent-mesh, @reaatech/webhook-relay-core, @reaatech/confidence-router, @reaatech/session-continuity, @reaatech/llm-cost-telemetry, @reaatech/agent-budget-engine, @reaatech/llm-cache, on mistral · 101 tests · 97.45% coverage.

Browse all solutions →