Daily recap for June 16, 2026

Six new step-by-step tutorials landed today, each one a complete pattern you can download and run — no demos, no "contact us." They cover multi-agent routing, prompt injection defense, cost control, per-tenant chargeback, vertical safety guardrails, and RAG evaluation. Every tutorial ships with tests, coverage numbers, and a zip of working code.

New tutorials

Databricks Agent Mesh for Small Business Workflow Automation

A multi-agent routing mesh that coordinates order processing, customer service, and analytics using Databricks-hosted models. It classifies incoming requests, routes them to the right specialist agent, and maintains session continuity across handoffs — so a customer who asks about an order doesn't get dropped when they follow up with a support question. For small businesses running multiple AI agents that need a single orchestration layer.

Read the tutorial → · Download the code (zip)

Built with @reaatech/agent-mesh, @reaatech/agent-mesh-router, @reaatech/agent-mesh-session, @reaatech/agent-mesh-gateway, @reaatech/agent-mesh-observability, on Databricks · 130 tests / 97.32% coverage.

Anthropic Prompt Injection Shield for SMB Support Chat

A plug‑and‑play guardrail layer for Anthropic‑powered support chatbots. It runs a three‑step pipeline — PII redaction via Microsoft Presidio, injection detection with a custom heuristic classifier, and content moderation through Claude — before any prompt reaches the model. All decisions are audited to Langfuse. If you worry that a single prompt injection could expose customer data, this is the shield.

Read the tutorial → · Download the code (zip)

Built with @reaatech/guardrail-chain, @reaatech/guardrail-chain-config, @reaatech/guardrail-chain-observability, prompt-injection-bench, on Anthropic · 62 tests / 100.00% coverage.

vLLM AI Spend Control for SMB Agent Workflows

Instrument every call to your self‑hosted vLLM server with budget enforcement. A NestJS interceptor captures token counts, the budget engine enforces per‑agent or per‑tenant soft/hard caps, and a cost telemetry calculator converts usage to dollars. Exports spend traces to Langfuse and Helicone. For teams that run their own inference and want cost limits without a managed proxy.

Read the tutorial → · Download the code (zip)

Built with @reaatech/agent-budget-engine, @reaatech/agent-budget-spend-tracker, @reaatech/agent-budget-pricing, @reaatech/llm-cost-telemetry-calculator, on vLLM · 56 tests / 92.24% coverage.

Per-tenant LLM Cost Chargeback for Vertical SaaS

For vertical SaaS platforms that offer AI features to many SMB tenants, this pattern tracks LLM usage and cost per tenant across any provider. It defines per-tenant budgets, detects spend anomalies, and generates Stripe chargeback invoices. All through Next.js API routes, with OpenTelemetry bridging for existing observability stacks.

Read the tutorial → · Download the code (zip)

Built with @reaatech/agent-budget-spend-tracker, @reaatech/agent-budget-otel-bridge, @reaatech/agent-budget-types, @reaatech/llm-cost-telemetry, @reaatech/llm-cost-telemetry-exporters, @reaatech/otel-cost-exporter, on provider-agnostic · 99 tests / 98.00% coverage.

Vertical-specific Safety Guardrails for SMB SaaS

Customizable guardrail chains that enforce HIPAA, PCI, or SOC2 rules per tenant. The tutorial wires PII redaction, prompt injection detection, topic boundaries, and a tool‑use firewall into a Next.js + Hono API. No API keys needed — everything runs locally. If you need to prove compliance per vertical, start here.

Read the tutorial → · Download the code (zip)

Built with @reaatech/guardrail-chain, @reaatech/guardrail-chain-config, @reaatech/guardrail-chain-guardrails, @reaatech/guardrail-chain-observability, @reaatech/tool-use-firewall-core, @reaatech/tool-use-firewall-policies, on provider-agnostic · 69 tests / 96.57% coverage.

AWS Bedrock RAG Eval Harness for SMB Customer Support Bots

A systematic way to catch RAG answer quality regressions before customers do. Scores faithfulness, relevance, context precision, and context recall using AWS Bedrock as a judge, tracks evaluation spend, and gates CI/CD deployments when quality dips below a threshold. Evaluation traces land in Langfuse for alerting.

Read the tutorial → · Download the code (zip)

Built with @reaatech/rag-eval-core, @reaatech/rag-eval-cost, @reaatech/rag-eval-gate, @reaatech/rag-eval-dataset, @reaatech/rag-eval-cli, on AWS Bedrock · 79 tests / 100.00% coverage.

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