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Product announcements, deep dives into how we deploy AI Jobs, and engineering notes from the One-Click-Flare team.

Looking for the product documentation instead? Head over to doc.hollowhost.com.

AI Paid Media Agent: A Human-in-the-Loop LangChain Design

AI paid media agent designed with LangChain to analyze advertising campaigns and keep human approval in the loop.

An AI paid media agent should not receive an account token and start changing campaigns. The useful design is less dramatic: collect data, validate it, explain performance changes, rank possible actions, and wait for approval. That workflow gives media teams speed without hiding decisions inside an autonomous loop. This guide maps that approach to LangChain components and provides a Python structure you can adapt to paid search or paid social accounts.

How to Build a Model Router in a Test Harness

How to build a model router that dynamically selects the right LLM for each request in a test harness

A model router chooses which LLM handles each request. The hard part is not writing an if statement. It is proving that the decision improves cost or latency without silently reducing answer quality. Start with explicit rules. Put them inside a test harness. Then measure every route, failure, and escalation against a versioned dataset before sending production traffic through it.

LangSmith Managed Deep Agents: 5 Production Trade-offs

LangSmith Managed Deep Agents dashboard showing an autonomous agent moving from local testing to production deployment with traces, memory, sandboxes, and human approval.

LangSmith Managed Deep Agents gives you a hosted path from local agent code to a production runtime. It also moves important operational decisions into a managed platform. That can remove infrastructure work, but it does not remove your responsibility for authentication, cost control, isolation, or failure recovery.

AI Customer Support Agent: From Internal Tool to Product

AI customer support agent evolving from an internal Snyk support tool into a customer-facing product feature.

Moving an internal support agent into a customer-facing product changes the risk model. Employees can recognize weak answers, work around missing context, and report failures. Customers should not have to. The available research does not include a primary Snyk source that validates its timeline or implementation, so this article does not claim undocumented details about Snyk. Instead, it breaks down the productization path implied by the case: constrain the workflow, preserve human control, and measure whether the customer’s problem was actually resolved.

AnythingMCP Deployment: Secure MCP Server Gateway

AnythingMCP secure deployment gateway connecting AI assistants to MCP servers, databases, legacy systems, and compatible tools

AnythingMCP can put one managed gateway between your AI clients and internal tools. That simplifies discovery and authentication, but it also creates a sensitive control point. A weak deployment may expose database writes, shared credentials, or unrestricted network access. This guide shows how to evaluate the gateway, prepare a self-hosted deployment, connect tools, and enforce production controls without assuming undocumented image names, ports, or environment variables.

Bifrost AI Gateway: Production Deployment Guide

Bifrost AI Gateway production deployment with a unified interface connecting AI applications to multiple model providers

A model gateway removes provider-specific code from your applications. It also becomes shared infrastructure that can interrupt every AI workload when misconfigured. A production Bifrost deployment therefore needs more than a running container. You need isolated administration, protected provider credentials, explicit routing rules, observable failover, and a rollback path. This guide takes Bifrost from a local smoke test to a multi-provider production architecture without treating the gateway as a black box.

LLM as a Judge: Reproducible Agent Evaluation

LLM as a judge comparing two AI agent responses with a rubric, factuality checks, and preference signals.

An LLM can review thousands of agent responses faster than your team can. That does not make its verdict correct. A useful LLM-as-a-judge pipeline needs observable criteria, versioned test cases, complete traces, and human validation. Otherwise, a formatting change can look like an improvement while factual errors pass unnoticed. This guide builds an evaluation workflow you can reproduce, audit, and use as a release gate.

AI Memory: Short- vs Long-Term Production Guide

AI memory architecture showing short- and long-term memory flowing between production AI agents and applications

AI memory is not one database attached to an agent. It is a policy for deciding what to retain, where to store it, and when to recall it. Keep everything in the prompt and token use grows. Persist everything and stale or conflicting memories contaminate later runs. A production design usually needs both transient session state and durable, searchable records. This guide shows how to separate them, choose an architecture, deploy it, and test whether recall actually helps.