Living agents

A prediction made today is wrong tomorrow if the world changed in between. That sentence reads as obvious; the industry treats it as inconvenient. A brand that surveyed as "safe" on Friday can be the subject of a viral controversy by Monday. A category that read as "growing" in spring can flatten by autumn after one quarter of recession headlines. A competitor that was "irrelevant" in February can buy a Super Bowl ad and climb into top-of-mind by Tuesday.

Static synthetic agents cannot account for any of this. They were created on a given Wednesday, with a given prior, and they will respond to your stimulus on that prior forever. The seams are well hidden. The agent will speak in the first person, in present tense, and produce a confident answer. The answer is just, without anyone telling you, slightly out of date.

In a category that moves slowly this is fine. Most categories that buyers care about do not move slowly. Pricing perceptions shift after one viral pricing comparison. Brand affinity moves after one ad cycle. Crisis sentiment moves in hours. If the synthetic stack you bought is built to be precise on stimulus and blind to context, you are paying for an answer that was correct last month.

Each Prism agent receives a daily feed. The feed is small enough that a person could plausibly absorb it in a real day, and varied enough that it looks like a day rather than a research instrument: a dozen news headlines, a few synthetic social posts from people in their demographic, a couple of competitor ads, a snippet of peer conversation. The mix is matched to who the agent is, where they live, and what they have already been seeing this week.

Each item becomes a memory. The agent does not just glance at a headline and move on; the headline shifts a small set of internal sentiments, the way it would for a person. Brands mentioned positively gain a fraction of warmth. Brands at the centre of a negative cycle lose a fraction of trust. Topics that are repeated, the way news cycles repeat, compound their effect. Topics that fade from the feed decay back toward the agent's long-run baseline.

When you run a simulation, the agent answers from the world it has been living in. It does not narrate the news at you. It just responds the way a person whose week has looked like that week would respond. The mechanism is hidden. The effect is visible: predictions that move, gradually, with the market instead of trailing behind it.

Each item the agent consumes shifts sentiment toward the topics and brands it mentions, by a small amount. A single headline does not move the dial visibly. A week of consistent coverage does. We weight bad news slightly more than good news, because real human attention does the same: negativity bias is one of the better-replicated findings in cognitive psychology, and any model that pretends otherwise stops looking like a real human inside a week.

Moods decay back toward baseline over time, because real humans do not stay in a mood forever. An agent rattled by a Tuesday news cycle is calmer by Friday, unless the cycle has continued. Topics that get repeatedly mentioned compound: an opinion the agent forms about a category in week one is reinforced or eroded by what they see in weeks two and three. Quiet weeks let the baseline reassert itself.

After 30 days, every agent has an opinion shape that reflects the world they have been living in, not the world they were born into.

Most AI-feedback tools model buyers as polished respondents: a job title, a personality prompt, a one-shot opinion. Prism agents go further. Seven layered systems, peer influence, stack drift, circadian mood, memory consolidation, engagement modes, switching cliffs, and quarter-end pressure, give simulated SaaS buyers the texture of actual people checking your landing page on a Tuesday night, not the polish of a sales-call transcript.

None of these are gimmicks. Each one closes a specific way that AI agents leak their syntheticness when you watch them long enough. The cumulative effect is an agent population whose 30-day, 90-day, and one-year behaviour you can show to a SaaS founder and have them recognise the buyers they actually sell to.

The public validation page now shows time-series accuracy: the same cluster validated quarterly against fresh real-survey data. If our living agents are working, accuracy holds steady or improves as the world shifts. If they were static, accuracy would decay between calibration cycles. The gap is the proof.

Every cluster we publish carries its time-series alongside its current number, so you can see for yourself how a Prism prediction has aged in the months since we made it. Read the latest pass at /validation.

We don't scrape real users. Synthetic posts are generated, labelled synthetic in our logs, and never pretend to come from real people.

We respect our news partners' attribution requirements. Every signal we pull keeps its source link. When the source is Actually Relevant, we also keep their curator framing, the relevance summary, the antifactors, because that framing shapes how a real reader would process the story.

We don't reproduce paid content as if it were earned, we don't strip attribution from journalism we ingest, and we don't silently train on customers' stimulus briefs. The agents are synthetic; the supply chain is honest.