The label is everywhere and mostly meaningless. Here is how to tell a company that builds working agents from one that has renamed its chatbot, and the questions that do the sorting for you.
An AI agent development company designs, builds and deploys software agents that carry out real business tasks, from scoping the use case to connecting the agent to your data and tools, adding human checkpoints, and running it in production. Choosing one is harder than it should be, because the term has been stretched to breaking point. The building of an agent is the easy half. The integration, the governance and keeping it working are where the money and the risk sit, and where most vendors quietly fall short.
The scale of the mislabelling is worth naming. Of the thousands of vendors claiming to offer agentic AI, Gartner estimates only about 130 actually deliver it. The rest are practising what Gartner calls agent-washing: a chatbot or a scripted automation with a new sticker on it. Since the label costs nothing, you have to judge on what the thing does, not what it is called.
This is not a market where any competent supplier will do. The failure rate is structural: over 40% of agentic AI projects will be cancelled by the end of 2027, and 88% of AI proofs-of-concept never reach widescale deployment. A company that has actually shipped agents into production has already hit the walls that sink those projects: data access, brittle integrations, governance, the handover to a human when the agent is unsure. There is also a plain build-versus-buy signal here. Externally sourced AI tools succeed about 67% of the time, more than double the rate of internally built ones, largely because specialists have already made and fixed the expensive mistakes.
You can separate the real from the relabelled with a handful of questions, and the quality of the answers matters more than the answers themselves.
A company that answers those plainly, with examples, is a different proposition from one that redirects to model names and buzzwords. The governance answer is the most revealing, and it is the one we treat as core rather than an afterthought, for the reasons set out in how we govern a workforce of AI workers.
The companies that ship agents rather than slides share a method. They start from a specific process, not a general ambition. They scope autonomy tightly and widen it only once the agent has earned trust. They build the human checkpoint in from day one instead of bolting it on. And they measure a real number so the project can be judged. None of that is glamorous, which is precisely why the agent-washers skip it. Our own approach to putting agents into an operation, and the AI agents for business overview, describe what that looks like in practice.
Hiring a company is usually the right first move, because the failure modes are specific and specialists have already learned them. Building in-house makes sense later, once agents are core to how you run, you have proven the first ones, and you have the team to keep them alive. Starting in-house on your first agent, with no scars to learn from, is the most reliable way to join the cancelled 40%. Prove the model with help, then bring it in-house if it earns a permanent place.
It designs, builds and deploys software agents that carry out business tasks: scoping the use case, connecting the agent to your data and tools, adding the human checkpoints, and running it in production. The building is the easy half. The integration and governance are where the real work is.
It is relabelling ordinary chatbots or scripted automations as agentic AI to ride the hype. Gartner estimates that of the thousands of vendors claiming to offer agentic AI, only about 130 actually deliver it. The label is cheap, so judge on what the thing does.
Ask what runs in production, not in demos. Ask how they handle data access, governance and the human-in-the-loop. Ask who owns the code and what happens when the agent is wrong. Vague answers to those questions are the answer.
Externally built AI tools succeed at roughly double the rate of internal ones, largely because specialists have already made the mistakes. In-house makes sense once agents are core and you have the team to run them, which is usually after the first ones prove out.
If you would rather see a working agent than sit through a demo, bring us a process and we will scope one, tell you where a human stays, and be honest if an agent is the wrong tool. The workflow automation guide covers the ROI side. To start, go to contact.
Bring the process you want an agent for and we will scope it honestly, show you where a person stays in the loop, and tell you plainly if a simpler tool would serve you better.
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