Business software has traditionally been evaluated through the experience it provides a person. Buyers compare interfaces, training requirements, mobile access, integrations and the amount of time an employee needs to complete a task.
That framework is no longer sufficient. AI agents are becoming another class of software user, and they require a different kind of product readiness. They need structured data, stable actions, narrow permissions, machine-readable policies and logs that show exactly what happened.
Microsoft has argued that enterprise software was built around the assumption that its primary user would be human. As agents begin to work across customer records, finance systems and productivity tools, vendors are being asked to support machine-speed use without losing the controls designed for people.
Start with access, not an AI label
A product does not become agent-ready because it adds a chat panel. The more important question is whether an authorized agent can retrieve the right information and perform a defined action through a documented interface.
Buyers should examine API coverage, event support, data freshness and permission granularity. Can an agent read only the records needed for a task? Can it create a draft without receiving permission to publish? Can a high-risk action require separate approval? Can access be revoked immediately?
Tools that offer these controls will be easier to place inside a governed workflow even if their built-in AI features are modest.
Deterministic actions matter
Agents use probabilistic models to interpret ambiguous goals, but the systems they call should behave predictably. A payment should follow explicit rules. A customer record update should validate required fields. A deployment should pass tests before it reaches production.
The strongest business stack will combine flexible reasoning with deterministic tools. It will let an agent decide which approved action is appropriate without allowing the action itself to become vague.
Context needs structure and ownership
Agent readiness also depends on data quality. A system should distinguish active policy from an old draft, customer consent from a marketing inference and final financial results from a working forecast. Metadata, versioning and clear ownership become part of the product’s value.
This requirement may favor systems that are already trusted as a system of record. It may also create room for infrastructure that connects records across several applications while preserving source and permission boundaries.
Observability is a buying criterion
When a person makes a mistake in an application, a manager may be able to reconstruct what happened from a few actions. An agent can complete hundreds of steps across multiple tools. Buyers need execution traces, cost reporting, error classification and a way to link actions back to the original request.
They also need controls for rate and scope. An agent that is allowed to update one record should not silently update ten thousand because a prompt was interpreted too broadly.
Consolidation will accelerate
Okta’s 2025 Businesses at Work report found that the average customer used more than 100 applications. The addition of agents can make that fragmentation more expensive. Every application needs a connector, permission model, data mapping and monitoring strategy.
Companies may respond by consolidating tools, but not solely to cut subscription costs. They will favor platforms that expose more of the operating environment through a coherent control layer. A smaller number of well-governed systems may be easier for both people and agents to use.
A practical 2027 checklist
Before renewing a core application, buyers should ask five questions: Can an agent access the necessary data without screen scraping? Are actions documented and deterministic? Can permissions be limited by task and risk? Does the system preserve an auditable trail? Can the company change models or agent platforms without losing access to its own data?
The business stack of 2027 will still need to serve employees well. But the tools that earn a durable place will also be legible, controllable and useful to the agents working beside them.