What Agentic AI Means for Your Software Buying Decisions

A renewal conversation that sounds different

A 70-person company renews its helpdesk, CRM or finance system every few years. The old evaluation was mostly about screens: which one staff found easier, which reports looked better, and the price per user. That approach is starting to choose the wrong software.

If, over the next few years, a growing share of routine work in those systems is done by AI agents, the questions change. How well can an agent use it? Will the vendor let your agent in, or only theirs? What does it cost when an agent rather than a person does the work? The interface still matters for the people who remain, but it is no longer the whole story.

How the buying criteria shift

APIs are the new user interface

An agent can only do what a system's API allows. A product with a polished interface but a thin API, one that can read contacts but not update deals, or can create tickets but not merge them, will limit every agent you ever build against it.

• Can every important action in the interface be done through the API?
• Are there rate limits that an agent working a busy queue would hit?
• Does it support MCP or publish connectors for common agent frameworks?
• Are webhooks available for the events you care about?
• Is the API included in your plan, or reserved for the top tier?

The last question is a common trap. Several vendors keep full API access for enterprise plans, which quietly doubles the price of being agent-capable. For more on evaluating AI-era vendors generally, see questions to ask AI software vendors.

Seat pricing meets agents

Per-seat pricing assumes work is done by people logged in. When one agent account does the work of several people, vendors respond in different ways: charging agents as seats, charging per action or per outcome, or restricting what non-human accounts may do.

• Per-outcome pricing can align cost with value, but definitions such as a resolved conversation need reading carefully.
• Usage or credit pricing is predictable only if you can forecast agent activity.
• Terms banning automated access by non-vendor agents can appear in otherwise ordinary contracts.

Model the cost of the next three years under two scenarios: the same team with little agent use, and a smaller team with heavy agent use. If the second scenario is dramatically more expensive, the vendor has priced in a way that works against you.

The lock-in question gets sharper

Vendors would like their own built-in agent to be the one doing work in their system. That is understandable, and their agents are often good. The risk is a system where only the vendor's agent has full access, trained on your data, priced as the vendor chooses.

Buy software that lets your agents in, not software that only lets its own agent out.

Check whether the contract allows you to use third-party or custom agents through the API, whether your data, including AI-generated notes and history, can be exported in a usable form, and whether your data is used to improve the vendor's models. These are ordinary contract points, but they carry more weight when agents are involved.

A checklist for your next software decision

1. List the tasks in this system you expect agents to handle within three years
2. Check that the API supports each of them, ideally by testing the key calls in a trial
3. Confirm that permissions can restrict an agent account to exactly those tasks
4. Model pricing under a heavy-agent scenario
5. Read the terms on automated access, AI data use and export
6. Try the vendor's own agent, and try connecting a simple external one
7. Ask what their API and pricing roadmap says about agents, and get the relevant parts in writing

When agentic AI tips the decision towards building

For most businesses, most systems should still be bought. Agentic AI does, however, make a custom system more attractive in a specific case: a core operational process where agents will do much of the work, where vendor pricing punishes that, and where off-the-shelf APIs do not cover what you need. There, a lean custom application designed around agents from the start can be cheaper over five years and fit much better. Our view on that trade-off is set out in how we decide between building, buying and integrating.

SpiderHunts regularly advises clients to buy, and we say so plainly when a product fits. When it does not, we build through our SaaS and product development or custom software work. For individual agents rather than whole systems, the related decision is covered in agent marketplaces: buy or build.

Frequently asked questions