Domain-specific agentic AI for an Amazon-seller community.

Problem

Generic AI tools cannot do Amazon operations. The answer to a question about a campaign requires three things at once: the operator playbook that defines what good looks like, the live account data the question is about, and the ability to act on that account safely. A model has none of these by default. The first lives in someone's training curriculum, the second is gated behind authentication, the third is not something a chatbot can do without a real interface and real guardrails.

First principles

Three things have to land together for AI to do operator work in a real domain.

Knowledge. Not the model's training. The actual operator IP from inside the domain. Years of curriculum, community discussion, frameworks, lessons. The model needs to draw from this, not from the web average.

Data. Live access to the operator's own account. Not aggregated, not a snapshot. The PPC numbers that are true right now, the inventory state, the change history. Each operator sees only their own.

Action. The ability to make a change on the live account, with the safety surfaces that make that change reversible and logged. Recommendations without action are a slower version of the same problem.

Surfaces

TitanAI is the engine. It is delivered through three surfaces, all running the same underlying architecture.

Three layers

Behind the surfaces, three layers of work.

Knowledge. The corpus is more than a decade of operator material from inside Titan (strategies, frameworks, community discussions, expert reviews), indexed and authority-ranked. The engine answers with what the community has actually proven works, cited at the source.

Brand data. Each operator's own Amazon account, wired in directly. PPC campaigns, search-query performance, ranking history, inventory, change log. Read-only at this layer. Scoped per operator.

Action. A set of safe, logged, reversible write actions on the operator's live account. Changes to campaigns, keywords, negative keywords, ad-group structure. Every action is recorded and surfaced to the operator.

Numbers

Within one month of release, across the three surfaces combined, the deployed agents have called more than 30,000 tools to read live data and execute actions on member accounts.

Observation

Two patterns surfaced.

Tool surface structure matters more than tool count. The first iteration exposed a tool per capability. The agent reliably picked the wrong combination. Restructuring to a smaller set of outcome-shaped interfaces, each composed of the capabilities it actually needs, dropped the error rate immediately. The pattern is documented in a separate field note.

Write actions require their own safety surface. Read-only access can be granted broadly with little risk. Write access cannot. Every write-capable tool was gated through a separate review process before being exposed. Zero operational incidents across more than five thousand write actions is the outcome of that process, not a property of the underlying model.