For much of the last decade, conversational AI was a polite front door. It answered questions and collected details, then handed the hard parts to people. In 2025, a different pattern is taking hold. Enterprises are shipping AI commerce agents that engage customers, coordinate internal systems, and complete tasks—always within policy limits and with a clear audit trail. These agents are judged by outcomes and risk, not just response time.
Industry observers say Prateek Batla's work represents a shift from conversational AI to accountable automation in commerce. "Chat is an interface. Commerce is a system," Batla says. "Agents connect the two by carrying accountability for outcomes."
One of the practitioners advancing this model is Prateek Batla, a product leader in enterprise AI and data-driven commerce. Over the past decade, he has led multi-team programs at large consumer technology and enterprise software companies, focusing on policy-aware assistants and agents for pricing, promotions, catalog operations, support, and integrity.
What Is an AI Commerce Agent
An AI commerce agent is software that perceives business context, reasons over policies and goals, and takes actions that change state in commerce systems. It can draft and route a price change, assemble a promotion with eligibility checks, or propose a support resolution grounded in verified knowledge. The common thread is control: every decision is logged, explainable, and reversible.
Batla's approach centers on graded autonomy. Agents begin in assist mode, then progress to suggesting actions, requesting approvals, and finally handling low-risk actions within strict limits. Autonomy is earned through evidence gathered in shadow and pilot phases, and expanded only when guardrails and playbooks are in place.
From Chatbots to Accountable Agents
Early chatbots optimized for a friendly answer. Agentic systems optimize for verifiable results. That shift is being driven by clearer policies and value-at-risk thresholds, better connective tissue into pricing engines, promotion planners, catalogs, order managers, and CRMs, and leadership demands for proof of impact and proof of safety in the same breath.
"Great agent experiences are mostly great plumbing," Batla says. "Observability, rollback, and governance are what make the experience feel effortless."
A retail tech analyst based in New York who tracks automation programs says the pattern has matured from pilots to production: "Prateek's graded autonomy model lines up with how large retailers balance speed with risk. It is pragmatic and it respects approvals and policy."
Case Snapshots
Examples from Batla's recent work illustrate how this approach scales:
Price change workflow:
Regional price updates can spend days in preparation and approvals. In programs Batla led, an agent assembled recommendations, validated them against policy, routed to the right owners, and recorded why each decision was made in audit tooling. The effect was shorter cycles, clearer ownership, and fewer last-mile surprises.
Promotions orchestration:
Promotions suffer when tooling is fragmented and checks are manual. Batla's pattern lets an agent score proposed offers for margin and eligibility, draft channel copy, and schedule with a documented rollback. Execution becomes steadier and policy application more consistent through peak periods.
Multilingual support and translation:
In multilingual support queues, agents translate conversations, ground answers in verified knowledge, and propose resolutions that humans can accept or edit. The result is lighter backlogs and more consistent responses across languages.
Operating Model and Governance
Batla describes a production model with clear gates and responsibilities:
- Product framing: Define the business goal, value-at-risk thresholds, and where humans stay in the loop.
- Evaluation plan: Establish offline tests and online guardrails before any agent handles real actions.
- Graduation path: Shadow first, then suggest, then approve with a human, and only then, bounded autonomy for low-risk scopes.
- Runbook and on call: Treat agents like services with alerts, dashboards, and response playbooks.
"If an agent cannot show its checks and alternatives, it does not act,"Batla says. Under the hood, a simple four-layer setup—perception, reasoning, action, and assurance—connects policies and data to the systems that run pricing, promotions, catalogs, and support.
The results, he explains, show up as steady improvements in speed, quality, and policy alignment rather than flashy metrics. Teams see fewer surprises, faster turnaround, and greater confidence in how AI decisions are made. "The goal is agents that are helpful, accountable, and boring in the best way," Batla adds. "When the right thing just happens within policy, that's success."
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