Search “AI agent companies” and you get five different businesses wearing one label. They are not substitutes: a vertical agent product, a framework vendor’s services arm, a global systems integrator, a boutique generative AI consultancy and an engineering staffing model solve different problems and fail in different ways. Pick by which failure you can absorb, not by which demo looked best.
The second point matters more. In most stalled agent programmes we see, the agent layer was never the bottleneck. Retrieval quality, data contracts, permissions and evaluation harnesses absorb 60% to 75% of build effort. That means the question “which AI agent company” is largely a question about whether the supplier has real data engineers on the bench.
The five categories
| Category | What you actually buy | Where it breaks |
|---|---|---|
| Vertical agent products | Configured software for one workflow (support triage, claims, SDR) | Your data model does not match theirs; customisation is billed as roadmap requests |
| Framework vendors with a services arm | Reference architectures on their own stack | Advice bends towards their platform; lock-in on orchestration and eval tooling |
| Global systems integrators | Programme management, methodology, indemnity | 3 to 6 month ramp, pyramid staffing, €1,200 to €2,000+ per day for the seniors you actually wanted |
| Boutique generative AI consultancies | A small, strong team for a fixed scope | Capacity risk, and pilots that stop at the demo because nobody owns the data pipeline |
| Engineering staff augmentation | Named senior engineers on time and materials, inside your repo | You must supply product direction and technical leadership |
Our position is the last row, so read the rest with that in mind. We describe the trade-offs across all four buying routes in more detail in our comparison of the four ways to buy AI engineering capacity.
Why the data engineering question decides the outcome
An agent is a loop over a model, tools and memory. The reasoning part is largely commodity now. What is not commodity is the plumbing underneath it, and that is why the same buyers searching for agent vendors end up hiring a data engineering consultant three months later.
Specifically, a production agent needs:
- Ingestion and chunking that survives document churn. Re-indexing 200,000 documents nightly is a pipeline problem.
- Permission-aware retrieval. The agent must not surface what the requesting user cannot see. This is row- and document-level access control, not prompt instruction.
- Tool APIs against systems of record. Usually the slowest item, because it touches ERP, CRM and ticketing owners.
- Offline evaluation sets and regression gates. Without these you cannot ship a prompt change safely.
- Cost and latency instrumentation per step. Token spend per resolved task is the number your CFO will ask for.
If a prospective supplier cannot name who on the team does items one to three, you have bought a demo. Background on the moving parts is in our primer on how AI agents actually work, and the build-side view sits in our page on agent development for production.
Five questions for the first call
- Who specifically writes the retrieval and ingestion code, and what is their day rate?
- Show me an evaluation suite from a delivered project, redacted.
- What happens to the codebase and the vector store if we end the engagement in month four?
- What percentage of the proposed team is above five years’ experience?
- Which part of this would you refuse to build, and why?
Question five separates engineers from sales. We expand the scoring method in our one-call vendor evaluation guide.
Cost shape
Senior AI and data engineers from Eastern Europe typically land in the €400 to €700 per day band, against €900 to €1,600 for Western European consultancies and considerably more for integrator partners. Current figures sit on our daily rate page. A four-engineer squad for a first production agent, roughly 12 to 16 weeks, is therefore a €150k to €300k decision rather than a €1m one.
When we are the wrong choice
Three cases. If your workflow is generic and a configured product covers 80% of it, buy the product; renting engineers to rebuild it is poor capital allocation. If you need a single supplier carrying regulatory indemnity for an audited process, use a firm that sells that risk transfer. And if you have no internal engineering owner, staff augmentation will drift, because we execute against direction rather than invent it. If you do have that owner and the constraint is senior data engineering capacity, the arithmetic favours renting it.