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Editorial

Unify Agentic AI in Martech: End‑to‑End Workflow Integration

4 minute read
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Learn best practices for combining agentic AI and human‑in‑the‑loop workflows in martech to break silos and maximize workflow efficiency.

The rise of agentic artificial intelligence, highlighted by Gartner as a top strategic technology trend for 2025, has become particularly prevalent in the martech industry. From campaign optimization to personalized content creation, the applications across the industry are broad. These AI systems, capable of multi-step, autonomous workflows, move past simple data retrieval, generation and task execution to automate increasingly complex processes, essentially mimicking the way humans work.

In the past few months, organizations rushed to integrate innovative agentic AI solutions, but they also encountered siloed workflows — fragmented proofs of concept (PoCs) made up of disparate agents unable to communicate with one another. Marketers rapidly realized it is no longer just about the technology integration or individual PoCs. The next phase of AI in martech is focused on the unification of these systems, with human-in-the-loop integration, to drive efficiencies and enhance workflows across functions.

Overcoming Siloed Agentic AI Workflows in Martech

As the hype around AI agents grew at the end of 2024 into 2025, organizations pushed to incorporate the newest wave of innovation. However, they ended up with handfuls of different PoCs and automated workflows that, while helpful for automating routine tasks, did not leverage the true power of agentic AI. The silos created between agents and PoCs prevented end-to-end automated processes as well as data and context sharing, which in turn also introduced inconsistencies across deployments. The result in many instances was poor user experiences, duplication of efforts and lack of scalability.

To evolve past these fragmented systems, developing new protocols that allow agents to discover, access and act on the right data is critical. For example, protocols such as Anthropic’s Model Context Protocol (MCP) that enables secure, two-way connections between data sources and AI tools, or Google’s Agent2Agent Protocol, which supports communication among agents, can help bridge the gap between systems.

Technical integration is a key factor, but it represents just one piece of the puzzle. True AI unification and success requires human oversight into AI-enabled workflows to maintain alignment with brand values, strategic objectives and regulatory standards. Human expertise is also needed for complex decision-making.

Embracing Human‑in‑the‑Loop AI

The martech industry has surpassed the initial rush and excitement, and it is now on the brink of an important shift to human-in-the-loop agents. Marketers have always had to do more with less, so the integration of AI into workflows was a natural progression; however, with AI reaching new levels of maturity, the reintegration of human oversight into automated tasks is growing.

The reality is that human expertise and judgment remain essential components for the application of AI in marketing. Expert contextual guidance is critical to teach advanced AI systems how to accurately and effectively navigate specific jobs and provide users with a greater sense of control in the process. There are two main aspects to this transition: digital transformation and context management.

Digital transformation is not new, but the concept is shifting to that of an ongoing project rather than a one-off task. Without prioritizing change management and reshaping workflows, AI risks becoming just a streamlining exercise rather than a tool for disruption. Processes may be more efficient, but the full potential is not realized if organizations fail to reshape their operations. Interoperability across the existing martech stack relies on a built-out digital transformation roadmap.

The second aspect focuses on context management. When faced with creating campaigns, launching experiences across digital channels, or delivering hyper-personalized content, marketers must stay true to brand identities, guidelines and values. Without the proper context to pull from, such as previous campaigns, existing content and brand toolkits, AI systems risk straying from brand compliance, ultimately risking customer dissatisfaction.

An organization must first understand its own data and how it is supposed to be used before feeding the most accurate version to AI systems in a way it can understand. Only then can it produce accurate, brand-aware outputs — a requirement for all marketing-focused activities.

Related Article: AI Isn't Ready to Make Unsupervised Business Decisions

Best Practices for Embedding Oversight Into AI Workflows

Identifying where human oversight is required and where it is not needed is another critical part of building an AI strategy. There is a balance between autonomy and control here. Completely autonomous systems will not succeed, but systems with humans in the loop at every step are also bound to be ineffective.

Learning Opportunities

There are a few key techniques for embedding oversight into AI workflows, such as approval checkpoints and human validation processes, to help organizations monitor, audit and maintain control:

  • Protocols for management and memory: AI agents must have robust mechanisms to maintain context and memory as they interact with humans throughout workflows. This allows AI to recall previous inputs, decisions and data while communicating between different agents or human reviewers. Clear protocols should also govern how data is transferred and maintained.
  • User notification and hand-off: When human intervention is required, timely notification systems are crucial. Systems should provide timely alerts with relevant context so that humans can easily pick up where the AI left off.
  • Personalized involvement preferences: AI systems should adapt to individual preferences for when and how humans want to be involved. Some may prefer minimal involvement, while others want transparency at each step. Conversational interfaces, data visualization tools and seamless integration with existing martech platforms can also enhance human-AI collaboration.

As AI capabilities continue to evolve and unify, the balance between automation and human control will be an ongoing consideration for marketing leaders. Finding the right combination is key for a successful transition as the martech industry stands at a pivotal turning point.

Organizations and marketing leaders are increasingly recognizing the potential of unified AI systems. By understanding the benefits and limitations of agentic AI, they are taking crucial steps to harness its promise for enhanced personalization, differentiation and efficiency.

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About the Author
Mo Cherif

Mo Cherif is Vice President, AI & Innovation at Sitecore, with over 20 years of experience at the intersection of digital experience, data science and cloud technologies. A passionate and curious leader based in Connecticut, Mo has worked across both the Middle East and North America, partnering with some of the world’s largest enterprises and government organizations to drive transformation through technology. Connect with Mo Cherif:

Main image: kirill_makarov on Adobe Stock
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