Over the past few weeks, a new open-source project, OpenClaw, has spread quickly across the AI community. Developers are experimenting with it, founders are building around it, and it is becoming a reference point in conversations about where consumer software is heading.
OpenClaw is not just another chatbot. It is an AI agent that can act on a user’s behalf and complete multi-step tasks rather than simply respond to prompts. People are already using it to reorder household items, schedule deliveries, manage subscriptions, and handle small recurring chores they would normally do themselves.
The important shift is behavioural, not technical. Users are beginning to delegate everyday decisions to autonomous systems.
Over the weekend, Sam Altman announced that Peter Steinberger, OpenClaw’s founder, will join OpenAI to help drive the next generation of personal agents. The move reinforces a strategic direction that has been visible for some time. Chat interfaces are evolving from assistants that reply into assistants that act.
For consumer brands, the shift is fundamental. When AI agents handle routine purchases, visibility moves from shelf placement to algorithmic preference. Product discovery happens inside conversational interfaces where brand equity, attributes, and pricing are compressed into structured signals that feed recommendation logic.
The brands that win will optimise how their products appear in AI-generated recommendations and ensure their commerce infrastructure integrates with protocols such as OpenAI’s Agentic Commerce Protocol.
How OpenClaw Works For Commerce
OpenClaw changes the role of software in a purchase. Instead of helping a user choose between options, it resolves the task itself.
The interaction starts with an outcome. A user does not search for products or open a retailer. They state an intent such as keeping a household item stocked or reordering something they previously liked. The agent converts that instruction into a structured objective informed by stored context like past purchases, price tolerance, and delivery expectations. The comparison phase happens internally, before anything is shown to the user.
The system then builds a plan. It identifies the relevant product, evaluates suppliers, checks constraints, and places an order. Rather than returning links, it continues operating until a real world confirmation exists. Completion is defined by a transaction, not a response.
Because the agent can interact with websites and APIs, it performs the steps a customer normally would. It logs in, fills baskets, selects delivery options, and confirms payment. Discovery, comparison, and checkout collapse into a single automated process. The user delegates the decision instead of navigating toward it.
Each completed action updates a persistent preference profile. Over time the agent learns acceptable brands, substitutes, and budget boundaries. Future purchases require less instruction because the system optimises for behaviour rather than prompts.
This is the important shift. In traditional ecommerce, products compete to be noticed. In agentic commerce, products compete to be selected. The deciding moment moves from a page viewed by a person to an evaluation performed by software.
What Steinberger’s Move Signals
The recent announcement that Peter Steinberger is joining OpenAI points to a clear next step for agentic commerce.
The technical foundations already exist. Commerce protocols allow models to interact with merchants and complete transactions. What has been missing is a dependable agent layer inside mainstream AI products.
Projects like OpenClaw demonstrate that layer in practice. They show an assistant moving beyond recommendations into execution.
The implication is simple. Autonomous shopping is likely to become native behaviour inside systems like ChatGPT. The remaining shift is integration. Once agents are embedded directly into everyday AI interfaces, purchasing stops being something users do and becomes something software resolves.
How Brands Should Prepare For Commerce Agents Like Open Claw
Agents do not have taste. They operate on signals.
Systems like OpenClaw evaluate products using structured criteria rather than persuasion. In practice they optimise for four things: constraint match, reliability, preference history, and utility.
If a product is hard for software to evaluate or execute, it will be skipped regardless of brand strength.
Below are practical ways to become selectable.
1. Make your product machine readable
Agents first check whether an item satisfies the request. That requires clear attributes, not marketing language.
Provide structured, consistent fields across every listing:
exact size and quantity
ingredients and dietary tags
compatibility information
delivery windows
subscription availability
clear variant naming
Avoid ambiguity. “Family pack” or “regular size” forces interpretation and increases rejection risk.
Agents select products they can verify without guessing.
Principle: if a machine cannot confidently confirm the constraint, the product effectively does not exist.
2. Optimise for successful execution
Agents prefer outcomes that reliably complete. A slightly worse product that always delivers will beat a better one that sometimes fails.
Improve operational signals:
accurate stock levels
stable product URLs and IDs
predictable delivery times
clean refund and replacement handling
minimal checkout friction
Failures retrain the agent. A cancelled order is not a lost sale, it is a long term exclusion from future automated purchases.
Principle: reliability compounds into default selection.
3. Design for repeatability, not discovery
Agents heavily weight past success. The first purchase matters less than the second automatic one.
Encourage behaviour that creates a stable preference:
consistent naming across retailers
persistent product identifiers
subscriptions or reorder endpoints
minimal SKU churn
predictable packaging and quantity
If the same product appears slightly different each time, the agent treats it as a new option and resets trust.
Principle: agents build habits faster than humans do.
4. Expose programmatic purchasing paths
A product page is designed for humans. Agents need callable actions.
Provide machine accessible purchase routes:
APIs or structured checkout endpoints
standardised carts
authentication that supports delegated access
stable deep links to specific variants
The easier it is for software to complete the transaction, the more often it will choose you.
Principle: being callable matters more than being clickable.
5. Compete on predictable value, not persuasion
Agents calculate trade-offs. They evaluate expected satisfaction against risk and price stability.
Favour consistency over occasional promotion:
stable pricing bands
clear pack economics
minimal hidden fees
transparent shipping thresholds
A product with slightly higher but predictable cost often wins over a volatile cheaper option because it reduces decision uncertainty.
Principle: agents optimise expected outcome, not excitement.
The shift is subtle but structural. Brands are no longer optimising for attention inside an interface. They are optimising for eligibility inside a decision system.
The winners in agentic commerce will be the products that software can understand, trust, and execute repeatedly without supervision.
Final thoughts
Agent commerce changes what it means to compete online. Brands used to optimise for visibility, getting seen and clicked by a person. Now the decision often happens before a human sees any options. An AI agent verifies, selects and completes the purchase on the user’s behalf.
The key question becomes trust. If a product is unclear, unreliable or hard to execute programmatically, it is skipped. No impression and no chance to persuade. The winners will be the products systems can confidently understand and repeatedly buy without supervision.
At Azoma, we run an Agentic Commerce Readiness Audit to show whether your products are selectable by AI purchasing agents. The question we answer is: If software started buying your category tomorrow, would it pick you? ➡️ Get in touch here for your free agentic commerce audit and find out the answer today!
Article Author: Max Sinclair