Microsoft licensing for Azure AI and Copilot features is complex and sometimes restricts access to enterprise or verified organizations.

Cameron Rohn passed on demos of agentic AI tools for enterprise clients due to excessive complexity and lack of centralized management.

Offer an enterprise-grade AI copilot integrated into Teams with centralized auditing and compliance guardrails managed via a cloud backend.

Copilot Studio is Microsoft's tool for building and managing AI copilots with integrated guardrails and Teams front-end support.

Azure provides an evaluator library for prompts similar to Langsmith that enables continuous prompt evaluations within enterprise environments.

Use Azure's evaluator library for continuous monitoring and evaluation of prompts similar to Langsmith's approach.

Microsoft's environment offers robust compliance and governance features that are attractive for enterprise AI deployments.

Integrating AI solutions into an existing Microsoft stack with Teams frontend and centralized backend management reduces shadow IT and ensures complia...Show more

Cameron recounts exploring Power Automate’s containerized VMs as a POC to bring AI automations into compliance within existing IT infrastructure.

Tom describes Process Miner as a Microsoft tool that records screen/video and voice to automatically generate process documentation using AI-driven do...Show more

Cameron suggests using Power Automate’s containerized VM environments to wrap AI-generated Python automations in audited, compliant infrastructure for...Show more

Tom argues Microsoft’s dominance in business desktops gives it a strategic advantage for distributing AI tools within enterprise Windows environments.

Tom notes Microsoft Copilot Studio has multiple agent-building interfaces, creating confusion due to several distinct Copilot types.

Cameron built a basic pipeline using an evaluator that overrides rows in an existing dataset for multiple experiments, tracking correctness rates, run...Show more

Cameron debugged prompt generation late at night in the Dallas airport bar, illustrating the ad-hoc nature of hands-on AI experimentation.

Anthropic Haiku can be swapped in via the playground for comparative testing against other LLMs, highlighting the ease of model interchangeability.

LangChain provides core tooling for running evaluations and experiments on LLM prompts, serving as the foundational layer for AI development workflows...Show more

Langsmith offers a highly configurable experimentation environment with support for custom evaluators, trace inspection, and API-driven runs.

Building automated evaluator hooks into CI/CD pipelines presents a market opportunity for tools that seamlessly integrate prompt testing into develope...Show more

High configurability, including setting max requests per experiment, demonstrates how mature the platform’s control features have become.