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Production

AI Agent Audit Logging: Tamper-Evident by Design

AI agent audit logging is the difference between telling an auditor "trust us, the log says so" and handing them a record they can independently verify wasn't edited after the fact. When an autonomous agent calls a refund tool, a delete_user tool, or anything that moves money or data, you need a durable answer to "who called which tool, when, and with what outcome" — and you need that answer to survive an insider with write access to the log file. By the end of this post you'll know why plain JSON logs fail that test, what an HMAC-chained audit trail buys you, and how to turn one on in a single line with Promptise Foundry's AuditMiddleware.

Circuit Breakers for AI Agent Tools: Resilience 101

Circuit breakers for tools are the difference between an agent that degrades gracefully and one that burns your token budget while a downstream API is on fire. When a tool starts failing—your payment provider times out, a search API returns 503s—a naive agent does the worst possible thing: it retries in a hot loop, waits out every 30-second timeout, and asks the model to "try again" until your bill and your latency both spike. By the end of this post you'll know how to wrap a flaky tool in a per-tool breaker, pair it with timeout and concurrency limits, and turn a trip into a clean, retryable error the model can reason about.

Deploying AI Agents to Kubernetes with Promptise

Deploying AI agents to Kubernetes is where most agent projects stall: the tool server runs fine on your laptop, but the cluster wants HTTP endpoints, real health probes, and metrics it can scale on — none of which a bare python server.py gives you. Promptise closes that gap with pieces you can wire in a few lines: a HealthCheck for dependency-aware readiness, PrometheusMiddleware for metrics, and promptise serve --transport http to run the server without boilerplate. By the end of this post you'll have a copy-paste path from a local MCP server to a running Kubernetes Deployment with liveness, readiness, and startup probes and a scrapeable /metrics endpoint.

Human-in-the-Loop Approval for AI Agents, Done Right

Human-in-the-loop approval is the control that lets an autonomous agent do real work — issue a refund, delete a record, wire money — without letting it do that work unsupervised. The instinct most teams reach for first is a client-side confirmation: the agent asks "are you sure?" and waits for a click. That feels safe, but it protects nothing, because the confirmation lives in the caller you happen to be using today. By the end of this article you'll understand why approval has to live on the server that owns the tool, and you'll have a runnable gate that denies irreversible calls until a human signs off.

LLM Tool Rate Limiting: Per-Client and Per-Tool Guide

LLM tool rate limiting is the part of production agent infrastructure that most tutorials skip, and it is exactly the part that pages your team at 2 a.m. An agent is not a well-behaved REST client that makes one request per user action. Give it a task and it will fan out five, ten, or fifty tool calls in a single turn, retrying and reformulating as it reasons. A flat "requests per minute" cap treats all of that as one undifferentiated stream, which is why it protects nothing. By the end of this guide you will know how to declare a rate limit directly on the tool that needs it, how the token bucket behaves under bursty agent traffic, and when a plain API gateway is the better tool for the job.

Multi-Tenant AI Agents: Architecture for SaaS

Shipping a multi-tenant AI agent means one deployment serves many customers, and the single worst thing that can happen is that Acme's data surfaces in Globex's session. If you are building AI features into a SaaS product, tenant separation is not a nice-to-have — it is the invariant your whole security story rests on. This post walks through why per-handler tenant checks fail, what a real isolation boundary looks like, and how to make cross-tenant leakage structurally impossible in Promptise Foundry with one server flag and a tenant_id that flows from your JWT all the way to storage.

Per-Tenant Data Isolation for AI Agents, Enforced

Per-tenant data isolation is the promise that Acme's data never surfaces in Globex's session — and it only counts if it holds at every layer your agent touches, not just the one you remembered to check. Most frameworks leave you to hand-roll a tenant check in each handler, which fails silently the first time one call site forgets. This post shows how Promptise Foundry makes tenant separation a structural invariant: a tenant_id that flows from the JWT claim into rate-limit buckets, audit entries, tool guards, memory, and — critically — the semantic cache scope. By the end you'll be able to turn on server-wide isolation with one flag and reason about exactly where the boundary lives.

The Production AI Agent Checklist: Ship Agents Safely

A production AI agent checklist is what separates a demo that answers once in your terminal from a system you can put in front of real users, bursty traffic, and an auditor who wants to know who invoked the refund tool at 2 a.m. The hard part of shipping an agent was never the prompt — it's everything around the tool calls: identity, load, failure, accountability, and isolation. This post is that checklist, mapped item by item to a concrete layer you can turn on today with Promptise Foundry. By the end you'll know exactly what production-ready AI agents require and how to add each layer without a rewrite.