The Accuracy Problem Agentic AI Cannot Avoid
Large language models are probabilistic by design. They generate plausible outputs, not guaranteed correct ones. For many business use cases — drafting communications, summarising documents, routing inquiries — that probabilistic nature is manageable. For tax compliance, it is not. A calculation that is accurate 98% of the time still produces errors at scale, and across thousands of tax jurisdictions with constantly shifting rules, even a small error rate compounds into significant exposure. Avalara's approach addresses this tension by treating agentic AI not as a replacement for structured logic, but as a coordination layer that sits above deterministic calculation engines. The agents handle the complexity of understanding context, selecting the right rule set, and orchestrating the workflow — while the underlying compliance engine is responsible for the precise numerical output. This separation of concerns is an important architectural principle: agentic AI at its most reliable is often an orchestration and reasoning layer, not a raw computation engine. The challenge of maintaining accuracy across real-time transactions also surfaces a governance point that any organisation deploying autonomous agents must internalise. Speed and reliability must be engineered in together, not traded off against each other. An agent that is fast but occasionally wrong is, in regulated contexts, simply not deployable.What This Means for Agentic AI Across Regulated Industries
The Avalara case is relevant far beyond finance and tax. Healthcare, legal, and real estate operations all involve moments where autonomous agents must produce outputs that are not merely useful but verifiably correct — a patient intake classification, a jurisdictional disclosure, a compliance-sensitive document. The architectural discipline Avalara is applying — separating orchestration intelligence from calculation certainty — is a template worth studying. For organisations evaluating where agentic AI fits within their own operational stack, the Avalara deployment is a useful reference point. It demonstrates that the value of an autonomous agent is not in replacing the precision of rule-based systems, but in adding reasoning, adaptability, and coordination above those systems. That combination — structured logic plus agent intelligence — is where durable accuracy at scale becomes achievable. Governance frameworks are also emerging as a central consideration. As explored in discussions around agentic AI governance standards, the organisations building for long-term deployment are those investing in auditability and accountability from the start, not as an afterthought.Precision as a Design Requirement, Not an Aspiration
The verticals Plus Bytes serves — healthcare, legal, real estate, hospitality, and golf and leisure — each carry their own version of the accuracy imperative. A missed appointment booking, an incorrectly qualified lead, or a miscommunicated follow-up carries real consequences for both the business and the client. The principle Avalara is demonstrating at enterprise scale applies directly: agentic AI deployed in these environments must be designed for precision from the ground up, with governance and auditability built into the architecture rather than layered on after the fact. Intelligence amplified responsibly is the only kind worth deploying.Source: siliconangle.com