The pace of artificial intelligence development has left many organisations struggling to keep pace. Whilst companies have spent months grappling with the implications of generative AI, the next wave has already arrived. Agentic AI, characterised by proactive assistants that operate autonomously on behalf of consumers, is poised to reshape the entire marketing landscape.

These are not the passive chatbots of yesterday. Agentic AI assistants make decisions, pursue goals and control purchasing choices without waiting for explicit commands. They act as intermediaries between consumers and brands, evaluating options, comparing offers and executing transactions. For businesses, this creates an entirely new category of customer: the algorithm itself.

The implications are profound. Traditional tools for managing digital presence, from robots.txt files dating back to 1994 to more recent innovations like llms.txt, were designed for a fundamentally different era. Forward-thinking organisations are already rethinking their infrastructure, data strategies and customer engagement models to thrive in this machine-to-machine future.

Understanding the Agentic Shift

The terminology surrounding artificial intelligence continues to evolve, creating confusion about what exactly constitutes an agent. Many systems currently labelled as agents are actually prompt-and-response models that remain fundamentally passive. They await user input, process the request and generate statistically probable answers.

Agentic AI operates differently. These systems act autonomously on behalf of users, making decisions and pursuing objectives based on learned preferences and explicit directives rather than waiting for a consumer to ask.

Consumer adoption of AI tools has demonstrated remarkable speed. ChatGPT reached one million users within five days of launch and surpassed 100 million users within months. Whilst agentic AI remains in earlier stages of consumer adoption, the right use case and experience design could trigger similarly rapid uptake. The challenge surpasses technological readiness alone, most companies lack visibility into how many AI agents already interact with their digital properties, accessing websites and evaluating content without organisations understanding the volume, nature or intent of these machine visitors.

An Opportunity to Modernise Digital Infrastructure

Traditional web governance tools were built for a simpler era. The robots.txt standard, introduced in 1994, provided web crawlers with basic instructions about which pages to access or avoid. For search engine optimisation purposes, additional tools emerged: meta tags, sitemaps, canonical tags and structured data.

This infrastructure served its purpose when the primary goal was helping human users find relevant content through search engines. However, it proves fundamentally insufficient for managing relationships with intelligent agents. These tools offer no mechanism for contextual control based on who is requesting information, why they need it or what value they offer in exchange.

The recent introduction of llms.txt files represents a modest step forward. This standardised markdown file provides large language models with structured guidance to a site’s most valuable content. Yet this approach remains essentially static, offering no capability for dynamic, real-time negotiation with incoming agents. A static file cannot differentiate between a consumer’s personal assistant seeking product information and a competitor’s bot attempting to scrape pricing data. The gap between current capabilities and future requirements is substantial.

Brand Agentic Assistants

The solution to managing machine-to-machine commerce lies in brands deploying their own agentic assistants to serve as operational gatekeepers for digital properties. These Brand Agentic Assistants function as the first point of contact for incoming agents, managing thousands of daily interactions and routing traffic appropriately.

A Brand Agentic Assistant protects and optimises bot traffic, distinguishing between friendly agents such as consumer assistants and partner systems and potentially malicious bots including data scrapers and competitors. The assistant’s role extends well beyond simple filtering. When a personal agent contacts a brand’s digital presence, the interaction becomes a conversation rather than a simple data request.

Consider a practical scenario: a consumer’s personal assistant seeks product recommendations. Rather than scraping the entire website, it contacts the brand’s agent. The organisation might request location data to provide accurate inventory information from nearby stores. It might request past purchase history to offer personalised recommendations. It might require authentication to access loyalty programme benefits. Each data exchange creates value for both parties whilst giving the brand control over what information it shares and under what conditions.

This approach requires rethinking data strategy at a fundamental level. Organisations must decide which content and data they will freely distribute for discoverability purposes, which they will exchange for specific value and which they will protect entirely.

Designing Effective Brand APIs

Traditional websites were designed for human consumption. Pages feature visual layouts, navigation menus, images and text formatted for reading. When an AI agent arrives at this digital doorstep, it must “read” and interpret everything to extract relevant information. This proves inefficient for both parties.

Brand APIs offer a more elegant solution for machine-to-machine exchange. Rather than forcing agents to scrape and parse human-oriented web pages, APIs provide structured access to data and content. This allows organisations to maintain precise control over data distribution whilst enabling agents to efficiently gather the information they need.

Developing an effective Brand API strategy requires answering several critical questions. First, what data should be made available for discoverability? As search behaviour shifts from traditional engines to large language models, visibility within these systems becomes crucial. Second, what value exchange model makes sense? A brand might provide basic product information freely but require authentication for pricing, inventory levels or personalised recommendations.

Third, which experiences should remain exclusive to direct human engagement? Some brand interactions benefit from richness that cannot be adequately conveyed through an agent intermediary. A furniture retailer might reserve its augmented reality room visualisation tool for direct website visits. These decisions require careful consideration of competitive dynamics, customer expectations and business objectives.

What are the Next Steps to Take?

The transition to M2M-native infrastructure will not occur overnight. A more prudent approach involves deliberate, phased implementation. Three foundational steps provide the essential groundwork.

Conduct an agent traffic audit. Most organisations currently interact with AI agents without realising it. The first priority involves making these interactions visible. Examining website analytics or deploying sophisticated bot detection tools reveals current agent traffic patterns. Establishing this baseline proves essential for measuring future changes and understanding the scope of agent engagement.

This audit should segment different types of bot traffic. Search engine crawlers serve different purposes than social media bots, friendly partner systems or malicious scrapers. Understanding who accesses your digital properties, how frequently and what they seek provides the intelligence necessary for developing appropriate response strategies.

Evaluate data readiness. Agentic AI architecture requires clean, accessible, well-structured data. Many organisations have spent years acknowledging their data challenges without addressing them. A thorough data readiness assessment examines both customer-facing and internal data sources. Are APIs modern, secure and properly documented? Can data be accessed in real-time, or do batch processes create delays?

This evaluation often reveals uncomfortable truths about technical debt and infrastructure limitations. However, the agentic era provides the catalyst many organisations need to finally address long-deferred modernisation projects.

Explore internal agentic proof of concepts. Looking inward before facing outward offers valuable learning opportunities. Identifying three to five high-impact internal processes suitable for agentic automation allows organisations to develop capabilities whilst generating immediate value. These internal deployments reveal skill gaps, data and technology requirements, and operational governance needs.

Internal use cases might include automating routine customer service inquiries, streamlining procurement processes or enhancing employee onboarding. Successfully deploying agents for these tasks builds organisational confidence and competence. These projects may also generate cost savings that fund subsequent external-facing agentic development.

Maintaining the Human Element

The rise of machine-to-machine commerce should not obscure the fact that humans remain on both sides of every transaction. Traditional marketing channels continue to generate substantial value even as new ones emerge. Billions of searches still originate from conventional search engines rather than AI assistants.

Brand recognition becomes increasingly vital in an agent-mediated world. When a consumer asks their assistant to “find the best electric car for me,” they receive very different recommendations than if they request “the best Tesla or Mercedes electric car.” Brand strength influences which options agents present to their users.

Human experience and customer service also deserve continued attention and investment. As time and attention become increasingly precious, organisations that deliver exceptional human interactions create lasting differentiation. AI should elevate and enhance customer experience rather than replace it entirely.

Agentic AI assistants will fundamentally reshape how customers discover, evaluate and purchase from brands. Those that prepare systematically will be positioned to thrive, whilst those that delay risk obsolescence. The path forward begins with three foundational steps: auditing current agent traffic, evaluating data readiness and deploying internal proof of concepts. These actions build understanding, reveal gaps and generate momentum. The machine-to-machine future is not distant speculation, it is emerging now, with early movers already gaining advantage.