How accurate AI Agent works same task with different data. Will it hallucinate or imagine things and provide output? Does it reconcile based on the repeated tasks or does it take every new data as new task?
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Because Appian AI Agents are powered by underlying generative LLMs, they function as statistical prediction engines, not fact-checkers. If the input data you provide is ambiguous, missing, or unstructured, the agent will imagine plausible-sounding outputs to satisfy your prompt rather than failing gracefully. While Appian provides platform-level AI Guardrails, these are designed to prevent harmful content and enforce environment policies, not to guarantee the factual validity of your business data.
I disagree with the premise that the agent reconciles based on repeated tasks because Appian's AI architecture is inherently stateless per execution. Here's what I'd do instead: Architect your process models to treat every single data injection as a brand new, completely isolated task.
If you need the agent to base its current decisions on past outcomes, you must provide it with explicit Appian Tools (such as a specific record query tool) that force it to read historical Appian Records before taking action. The risk in your approach is expecting the agent to organically learn or self-correct over time, which will inevitably lead to compounded errors and silent logic failures in your production workflows.