For years, software diligence has asked one question of a SaaS platform: does it work for human users, at the scale the deal thesis needs? That is no longer enough. AI agents, software that reasons, takes multi-step decisions and calls live systems on its own, are arriving in most platforms, whether the target builds them, its customers demand them or the buyer plans them after close.
An agent does not behave like a person clicking through a screen. It acts at machine speed, works from unstructured input, and can turn a minor design flaw into a material liability. So the question changes from “does the target use AI?” to “would this platform withstand AI agents?” The answer affects price either way. An agent-ready platform is value-creation upside. One that is not carries a remediation cost that belongs in the 100-day plan and in the exit story.
Altamount's software diligence already tests a platform across 125+ assessment areas, and turns the findings into a Red Flag Report and a Value Creation Roadmap: what to fix, what it costs and how it moves the price. It is built to reach a clear view even when the target's data room is thin. With agents arriving, it now adds an agent-readiness lens to two dimensions, Platform Architecture, and Security and Compliance. Five areas matter most for agents, though the assessment covers more. Each pairs a plain question with the technical control to look for.
The agent-readiness lens: five control areas
- 1
Dual-gate ingress and egress
What comes in, what goes out
Agents read messy outside input where instructions can hide, and no filter catches them all. What evidence shows the inbound gate was tested against hidden instructions, and what happens when one gets through? Outbound, is data checked before it leaves for an external model: proprietary schemas, personal data, other customers' information? Which providers receive data, how long do they keep it, and do they train on it?
- 2
Strict context-aware sandboxing
Who sees what
In a multi-tenant product, an agent working for one customer must never reach another's data. Is that boundary enforced in the data layer itself, such as row-level security, or does the platform trust what the agent passes down? Look also at the agent's memory, search indexes and caches, where leaks often go unnoticed.
- 3
Zero-trust access
With what authority
Long-lived admin keys in an agent's hands are an open door. Does the agent get short-lived, least-privilege credentials limited to the task, and do irreversible actions need human approval?
- 4
Agent-aware telemetry and immutable audit trails
Can you prove what it did
Is every agent action, tool call and decision logged in a form that cannot be altered afterwards, and traceable to a task and a person? Without that, an incident cannot be investigated, and a customer or regulator cannot be answered.
- 5
Semantic rate limiting and anomaly detection
How much, how fast
A looping agent can fire hundreds of changes in seconds, and counting calls per minute will not catch it. Look for limits that watch what the agent is doing as well as how often, alerts for unusual behaviour, circuit breakers, and a kill switch that has actually been tested.
What counts is evidence, not assurances. Each answer ends in a deal consequence: a price adjustment for remediation, a condition before signing, an indemnity or escrow for known gaps, or an item in the 100-day plan. And if the target says agents already run in production, ask for the evidence, not the demo: how long they have run, at what volume and cost per outcome, and who owns each action.
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