The day's SaaS news was all about the plumbing around AI agents

What we're tracking

• •Assume an agent will eventually delete something you need An AI agent is software that takes actions on your behalf. Some of those actions are destructive: overwriting a record, emptying a field, removing a row. A human doing the same job hesitates before deleting, because a human understands what the data is for. An agent does not hesitate in the same way. That means the safety you rely on cannot live inside the agent. It has to live outside it, in the form of a copy you can restore from.
• •Evaluate agents while they run, not once before launch Traditional software is tested before release and then behaves predictably. An agent does not. Its behaviour depends on the inputs it sees each day, the cost of each call, and the model behind it, all of which change. A test that passed last month tells you little about what the agent is doing right now.
• •Insights that never leave the tool they were created in There is a quieter failure mode than an agent deleting data, and it costs businesses more often. An agent produces a genuinely useful insight, and then nobody else sees it, because it stays inside the one interface where it was generated.
• •Giving an agent your context without losing control of it For an agent to be useful it needs to see your material — your projects, documents, and prior decisions. The risk is that connecting that material to a general assistant scatters copies of it into places you no longer govern.
• •The data an agent reads decays before it reads it Every point above assumes the agent is working from good data. Often it is not, and the decay is not a single failure but a series of small ones across a pipeline. SaaStr covered a company, Sumble, whose entire pitch is this problem: why data that starts 80% accurate turns into 33% accurate by the time an agent uses it, and how one customer, Nooks, found 700 accounts it did not know about [5].