October 22, 2025
We were testing agents this week when GPT-5 threw an error we didn't expect.
`"Unsupported parameter: 'temperature' is not supported with this model."
Temperature controls randomness in AI outputs. Set it to zero, you get deterministic responses. Generally same input, same output.
That's evidence you can trust.
When we certify agentic workflows at Swept AI, we run multiple prompts/tests through evaluation frameworks based on your chosen level of statistical significance (± 10% or better). We show you identical outputs as many times as you want. That becomes the proof that gets through security reviews.
Without temperature control, the industry loses repeatability.
The variance itself becomes a risk factor we have to account for in monitoring systems. And in healthcare or insurance, that variance can hallucinate drug dosages or incorrect premiums.
Multiple developers have hit the same wall. OpenAI's API now only supports temperature=1 for GPT-5. The technical reason makes sense: multi-pass reasoning breaks if you force deterministic paths.
But here's what's happening in production.
Engineering teams discover this when they try to set parameters and get errors. It doesn't reach the CISO or compliance level fast enough. AI projects move forward with GPT-5 because it's the newest, most capable model.
Then three months in, during security review, someone asks: "Can you demonstrate consistent behavior?"
By then they've built their whole workflow around it.
The teams that know are possibly switching back to older models or looking for alternatives, especially open weight models. But once older models deprecate, we're going to see stalled implementations or companies using models that can change from under them, necessitating AI Supervision.
The gap between "this model is powerful" and "this model is certifiable" is widening.
We should be using controllable models as evaluation layers for uncontrollable production models. The evaluator doesn't need to be smarter. It needs to be consistent. It verifies outputs meet criteria: schema compliance, prohibited content, expected patterns.
That's a workaround we shouldn't need.
Organizations with budget and compliance requirements need predictability. They need to produce evidence. They need to show buyers, regulators and boards that systems behave consistently.
When you remove temperature control, you're removing ability to generate that evidence. Swept AI can help with this problem.
If the tradeoff is between a more intelligent model that can't be shipped and a less capable model we can certify, teams will choose the one they can actually deploy.
Build advanced capabilities. But don't sacrifice the operational controls that make AI supervised and reliable enough to use.
