AgentStack is an independent publication for AI, operations and product leaders who are responsible for making enterprise AI work. We cover AI agents, copilots, AI platforms and workflow automation with sourced, practical analysis. Every piece is written to help you decide what to build, what to buy and how to run it once it is live.
[FOUNDER INTRO: one or two sentences on who started AgentStack and the experience they bring. Example: “AgentStack was started by [FOUNDER NAME(S)], who [RELEVANT EXPERIENCE].” Delete this line if you prefer not to name founders.]
Why we started AgentStack
Enterprise AI moves faster than the advice written about it. Launch posts, analyst decks and social threads arrive every day, yet very little of that material tells an operator what an agent costs to run, where it fails or how it fits an existing stack. Too much coverage repeats a press release or ranks tools the writer never opened.
We built AgentStack to be the resource we wanted when we had to make those calls ourselves. Our goal is for a head of AI, a COO or a product lead to finish an article with a clearer decision than they started with. That means showing our sources, naming the trade-offs and being honest about what we could and could not verify.
AgentStack is operated by [COMPANY LEGAL NAME] and runs as an independent editorial publication. [OWNERSHIP STATEMENT: confirm that no AI vendor, platform or investor owns AgentStack or directs its coverage, then keep this sentence: “No AI vendor, platform or investor owns AgentStack or directs our coverage.”] Some articles include paid placements from companies we write about, and every one of them is labeled. Our Editorial Standards page explains exactly how that works.
What we cover
We organize our coverage around the jobs leaders do when they adopt AI: build, connect, operate, govern, buy and justify.
- AI agents: We review agent platforms and function-specific agents for customer service, IT and HR, sales, finance and voice.
- Agent frameworks: We compare the frameworks and SDKs teams use to build agents, along with the architecture patterns behind them.
- MCP and agent protocols: We explain MCP, A2A, agent skills and the standards that connect agents to tools and to each other.
- AgentOps: We cover observability, evals, AI gateways and cost control for agents running in production.
- AI governance and security: We cover agent security risks, oversight models, internal policy and regulation.
- Workflow automation: We compare no-code automation, RPA and integration platforms that are adding AI steps and agents.
- Copilots and AI assistants: We evaluate workplace copilots, enterprise search and AI coworkers that teams use every day.
- Enterprise AI strategy: We publish guidance on pricing, ROI, adoption, operating models and industry use cases.
- News and analysis: We report launches, funding and acquisitions, and we explain what each change means for buyers.
Our formats include buyer guides, comparisons, reviews, explainers, field notes from real deployments, benchmarks and teardowns, and operator interviews. We also publish practical templates such as RFP questions, evaluation scorecards and rollout plans.
Who we write for
We write for AI and ML leaders, operations leaders, product leaders and the technical buyers who evaluate agent frameworks, orchestration and AI platforms. Our readers usually own a budget, a rollout or a production system. They need specifics they can bring into a planning meeting or a vendor call.
What we don’t cover
AgentStack is built for teams deploying AI inside organizations. We don’t publish consumer AI app roundups, personal productivity prompt tips, AI stock picks or investment commentary. Nothing on this site is legal, security, compliance, financial or procurement advice for your specific situation, so check important decisions with a qualified professional.
How we work
We start with primary sources such as official documentation, pricing pages, changelogs, model cards, protocol specifications and regulatory texts. Where we can, we test products ourselves and say exactly what we tested and how. Where hands-on testing is not possible, such as with enterprise-only platforms behind a sales process, we say so and explain what our assessment is based on.
Every article on AgentStack carries the name of the person who wrote it. [AUTHOR LIST: add each confirmed author’s name, beat and author page link, for example “[AUTHOR NAME] covers [BEAT].”] As the publication grows, we will add contributors who have built, bought or run AI systems inside real organizations.
Our promise
- Honest assessments: If a well-known tool falls short for a common use case, we say so, and if a smaller one does the job better, we explain why.
- Respect for your time: Every article should leave you with something you can use in a decision, a rollout or a review.
- Labeled commercial relationships: Paid placements, sponsored articles and affiliate links are disclosed where they appear.
- Current information: Pricing, features and regulations change quickly in this space, so we update articles and note when we do.
Say hello
Do you have a question, a correction or a topic you want us to cover? We read every message. Email us at [CONTACT EMAIL].