Shopper Agents that make life easier for everyone

The old way of building ecommerce solutions held us back. We ourselves struggled to build ecommerce solutions in the past — we had to build an ecommerce app, a website, and a tablet app, and test them separately. It was a nightmare.

We do not want other businesses to face this nightmare. We want building, testing, and deploying ecommerce solutions to be effortless. We want to reduce the time from building an ecommerce store to selling ecommerce products.

That is why we are building an Agentic Commerce Infrastructure for ecommerce businesses at AFCP AI. Using our platform, businesses can launch and deploy a shopper agent that their customers can trust. The shopper agent can help their customers shop safely and seamlessly, just like a friend. AFCP AI leverages efficient agents to foster human-AI collaboration in a sustainable manner for ecommerce businesses and consumers alike.

Agentic shopping — which includes agents collaborating with each other, agents handling all repetitive shopping tasks, and shopping over voice — will become the common approach used by customers of businesses to shop online. Agents will synchronize the world of commerce, becoming almost like electricity.

That is what our platform is about. An Agentic Commerce Infrastructure here to help retailers build shopper agents. Our platform's goal is to be the largest provider of agents for commerce. These agents are seamless to integrate and provide immense value to businesses, consumers, and partners.

The current internet is slow, fragmented and a big mess. Problems with identity, security and governance remain largely unsolved. To understand where we are going, it helps to trace where we have been.

During the early computing revolution, computers solved concrete, bounded problems: typing, reading, doing math. This was followed by the gaming revolution, where people began experiencing more on computers, not just doing. We are now in the AI revolution, and we believe this revolution is fundamentally about agents.

The world will look very different with agents in the picture. Agents must be reliable, affordable and scalable. To get there, we must use the old internet — Web1, Web2 and Web3 — as the backbone, but build an agentic layer on top for the experiencing part. Consumers and businesses can then interact directly with agents through voice, text or video.

Reimagining the old internet as an agentic one means solving four foundational requirements:

• 1
  Accessibility of agents — agents must be reachable by anyone, anywhere, across every device and channel.
• 2
  Identity of agents — every agent must have a verifiable, trustworthy identity so people know who they are dealing with.
• 3
  Security and privacy of agents — interactions must be safe, data must be protected, and agents must not become vectors for harm.
• 4
  Control back to consumers — agents must empower people, not extract from them. Consumers must remain in charge of their own data and decisions.
• 5
  Trustworthiness of agents — agents must behave honestly, transparently and predictably. A trustworthy agent earns repeated use; an untrustworthy one destroys the relationship in a single interaction.

Tim Berners-Lee envisioned an internet where consumers had complete control over their rights on data. Building an agentic internet with blockchain technologies can move us closer to that path — and that is the direction we are building towards.

We believe the world needs Efficient AI — AI that is like electricity, scalable, affordable and reliable. We must build AI that consumes fewer chips, compute and energy. Experiential AI can help us get there, by helping us deploy Agents which are efficient for the world.

Compute and energy costs are rising making LLMs unsuitable for efficient scaling in the long run. This means a new hybrid AI architecture is needed for efficiently carrying out thinking, planning, reasoning or acting.

This is akin to the DC distribution crisis of electricity. AI that is built in an LLM-first approach is analogous to DC.

We do not need electricity (AI) based on DC (LLM-first paradigm), we need AC — an efficient agent-first approach. Efficient AI can help us deploy AC like Agents for the world.

For this foundation to exist, we need an efficient Agentic architecture without relying heavily on models.

• 1
  Models cannot scale with the real world. They lack continual learning and memory. We can solve this by building Agents that continually learn, improve and perform intelligent decisions and actions.
• 2
  Generative AI is using a lot of training and inference compute. This is not needed. We can reduce this by 10x by implementing better strategies to build AI agents that can scale. We aim to consume 10x less infrastructure, compute and energy when compared with models.
• 3
  An agentic paradigm is simple. It consumes less energy, less compute, less capital, learns by experience continually, can be governed, is safe and reliable. It considers the human brain as the standard for building AI. We also believe agents can be decentralized and democratized rather than centralized.
• 4
  This new paradigm also needs clean, white-labelled data from Ecommerce data providers. This data can help us build a reliable agent that customers can trust.
• 5
  The new approach is a type of Experiential AI, not Generative AI. The architecture can be hybrid and use components of Generative AI, but without being restricted to it.

We at AFCP AI are working on a new approach for building our Agentic commerce infrastructure based on Experiential AI.

The last revolution was Generative AI. The new Agentic future needs an Experiential AI revolution. Building agents from an Experiential AI standpoint will be more relevant since consumers and businesses like to experience more. Experiential AI is all about building human-first, collaborative and self-learning agents for the world — the way to build agents that are reliable, affordable and scalable.

Experiential AI uses shopper agents that are not only active helpers for consumers but also interactive and fun to be with. Our shopper agents can make corrections in case a mistake happened, continuously learning with the aid of human intervention and therefore becoming self-adaptive.