![\"Martech](\"https:\/\/chiefmartec.com\/wp-content\/uploads\/2025\/01\/martech-ai-agent-battle-1456px.jpg\")
<\/a><\/figure>\nWelcome to 2025! It\u2019ll be the Year of AI Agents<\/strong> in martech, in word and deed. Here\u2019s what I think that means\u2026<\/em><\/p>\n From the moment there was more than one software product in a company\u2019s tech stack, there\u2019s been jockeying to determine who was boss.<\/p>\n If the tech stack were an orchestra, which product would be the conductor?<\/p>\n Whichever one directed the flow of data among apps and determined which function in which app was called when \u2014 literally orchestrating the tech stack \u2014 would wield a position of great power.<\/p>\n This is why it was a strategic error for marketing suites of the early 2010\u2019s to downplay integrations with other products in the burgeoning martech landscape<\/a>. They pretended other products weren\u2019t necessary or important. To have strived to be the conductor would have been to acknowledge the value of those other apps. They didn\u2019t want to concede that point.<\/p>\n As Julia Roberts\u2019 character in Pretty Woman<\/em> chided, \u201cBig mistake. Big. Huge!\u201d<\/p>\n Suites\u2019 failure to seize the baton created an opening for a whole new category of products known as \u201ciPaaS\u201d \u2014 integration-platform-as-a-service \u2014 to step up to the podium and build multi-billion dollar companies, such as Mulesoft, Workato, and Zapier. iPaaS embraced the diversity of tech stacks and empowered people to automate digital operations at a level above individual products.<\/p>\n The suites finally realized that they were becoming a spoke on the edge of the stack graph instead of the hub at its center. Many switched to platform strategies and pursued building app ecosystems. Those who didn\u2019t slipped into irrelevance. Salesforce acquired Mulesoft. HubSpot acquired PieSync.<\/p>\n But the barn door had already been open, and the cows never fully came home. To this day, iPaaS products \u2014 many of which have evolved into \u201centerprise automation platforms\u201d \u2014 retain a strong presence in most marketing tech stacks. They\u2019re co-conductors of the orchestra. For some use cases, the marketing platform calls the shots. For others, the enterprise automation platform n\u00e9e iPaaS does.<\/p>\n Ironically, history nearly repeated itself a few years later. CDPs \u2014 customer data platforms \u2014 arose between 2015-2020 because most marketing suites\/platforms were still not open enough or flexible enough with their data models. This gave CDPs an iPaaS-like opening, and they stampeded through it.<\/p>\n But the marketing suites\/platforms weren\u2019t as resistant to respond this time. Most acquired a CDP or built CDP-like capabilities by the 2020\u2019s. (Some overcompensated by purchasing or building multiple<\/em> CDPs, but that\u2019s a little inside baseball.) Meanwhile, in a surprise twist, cloud data warehouses\/lakehouses disrupted the whole CDP landscap<\/a>e<\/a>. That\u2019s a story for another day though.<\/p>\n Which brings us to 2025. The modern martech stack has multiple platforms vying to be the conductor at the \u201ccenter\u201d of it all. Many marketing ops teams try to standardize on one to automate workflows across their toolset, although which platform they choose \u2014 CRM, MAP, CDP, CDW, etc. \u2014 varies from company to company. But nearly 1\/3 also use an iPaaS\/enterprise automation platform too.<\/p>\n Complex? Somewhat. Manageable? More or less.<\/p>\n But brace yourself. It\u2019s about to get waaaay more complex with \u2014 drum roll<\/em> \u2014 AI agents<\/strong>.<\/p>\n While there\u2019s lots of debate about what qualifies as a \u201ctrue\u201d AI agent \u2014 especially around how much decision-making autonomy it has \u2014 one characteristic that almost all agents share: tool use<\/em>. They can interact with other software, either through APIs<\/a> or by simulating a human user\u2019s clicks and typing, in an RPA-like fashion. They apply such tool use to read or write data from different sources, take actions in other apps, or even make requests to other agents. The latter is the vision of multi-agent networks.<\/p>\n AI agents can take on tasks, processes, or even more open-ended goals. They automate digital operations across different products in your tech stack in an integrated fashion.<\/p>\n Except for the goal-seeking twist, that sounds familiar, no?<\/p>\n AI agents reverberate with the echoes of iPaaS and workflow automation platforms. In fact, many of the vendors in those categories have transformed into AI agent building platforms. The difference? Workflow automations ran according to deterministic rules and steps that were programmed by a human. AI agents apply their own intelligence to decide which tools to call when \u2014 without an explicit plan dictated to them by humans.<\/p>\n Existing martech SaaS platforms aren\u2019t dragging their feet to the AI agent party either. Salesforce has swung their narrative pendulum all the way to Agentforce<\/a>. HubSpot has its Breeze AI agents<\/a>, and co-founder Dharmesh Shah launched agent.ai<\/a>, \u201cthe professional network for AI agents.\u201d Microsoft has incorporated Copilot Studio for building AI agents in Dynamics<\/a>. Braze<\/a>, Bloomreach<\/a>, and Zeta<\/a> have announced their own AI agents too. <\/p>\n Now, if we were simply dealing with existing iPaaS\/automation vendors and ecosystem-oriented SaaS platforms morphing into AI agent platforms, that would be challenging enough. In the short term, digital ops will become a tangle of old deterministic workflow automations intermingled with new intelligent agent automations. We\u2019ll have to migrate to new ways of architecting marketing operations processes accordingly. But we\u2019ll get the upside of agents doing more work autonomously, reducing the reams of rules maintained by hand. (Hallelujah, right?)<\/p>\n However, existing SaaS and iPaaS\/automation companies are not<\/em> the only players in the pit. Multiple new categories of AI agents have joined in, each bringing their own take on intelligent orchestration of individual productivity and\/or organizational process automation.<\/p>\n Here\u2019s a quick tour of the other agent contributors in the map above\u2026<\/p>\n The core AI engines from Anthropic, Google, Meta, OpenAI, et al., all support agent development, with \u201cfunction calling\u201d for tool use as a basic primitive. Anthropic\u2019s recently announced \u201ccomputer use<\/a>\u201d capability and Model Context Protocol<\/a> are exciting steps forward in foundational AI agent capabilities.<\/p>\n Agent developer frameworks such as Autogen, AutoGPT, Camel AI, Griptape, LangChain, and LlamaIndex all help facilitate companies building their own custom agents. If you can code with APIs \u2014 and with AI-powered developer tools such as Cursor.so<\/a>, GitHub Copilot<\/a>, and Replit<\/a>, increasingly anyone<\/em> can code \u2014 you can build your own tailored AI agents in a matter of days or hours.<\/p>\n Want even more help in building your own AI agents? There\u2019s a whole new category of no-code\/low-code agent builders: CrewAI<\/a>, Lindy<\/a>, Relevance.AI<\/a>, Sema4.ai<\/a>, SmythOS<\/a>, and Tektonic.AI<\/a>, to name just a few. The boundary between these and evolved iPaaS\/workflow automation platforms is extremely fuzzy. I\u2019d merge them, except it\u2019s worth pointing out how many new<\/em> native AI agent builder platforms have emerged in this category.<\/p>\n On the far other end of the spectrum, Anthropic, Google, Microsoft, and OpenAI are all incorporating tool use in their end-user products such as Claude, ChatGPT, Copilot, and Gemini. Increasingly, using Gemini inside Google Workspace or Copilot in Microsoft 365 products such as Excel and Teams will perform agent-like automations for individuals. There will be increasing overlap between personal productivity and business process automation.<\/p>\n Independent browser-based AI automation tools such as Bardeen<\/a> and Axiom.ai<\/a> will be a part of the mix as well. Whatever web-based software a user has access to, they can create AI agents (or pseudo-agents) to automate their work with those apps \u2014\u00a0even if the vendors of those products have done nothing expressly to enable that.<\/p>\n There\u2019s a special category of agents that have emerged to leverage reinforcement learning<\/a> \u2014 a sub-branch of machine learning \u2014 to autonomously optimize personalized marketing campaigns and programs. OfferFit<\/a> is a pioneer in this space (disclosure: I have been an advisor to them<\/em>). Aampe<\/a> and composable CDP Hightouch<\/a> have also entered the category, which is coming to be known as \u201cAI Decisioning.\u201d<\/p>\n Last but definitely not least, there\u2019s a massive wave of role-specific AI agent tools, especially for sales and customer service. Representative AI agents for sales include 11x<\/a>, AISDR<\/a>, Artisan<\/a>, Momentum<\/a>, regie.ai<\/a>, and Rox<\/a>. Customer success AI agents include Decagon<\/a>, MavenAGI<\/a>, and Sierra.ai<\/a>. Of course, existing SaaS platforms are competing with their own role-specific agents, such as those from HubSpot, Intercom, Salesforce, and Zendesk.<\/p>\n The thing all these different AI agents have in common?<\/p>\n They\u2019re all automating interactions \u2014 increasingly autonomously \u2014 with the products and platforms in your stack. Each thinks it\u2019s orchestrating its piece of the symphony. But will there be one conductor that sees across the entire orchestra and harmonizes all these players? Expect intense vendor competition for that position.<\/p>\n<\/a><\/figure>\n<\/a><\/figure>\n<\/a><\/figure>\n<\/a><\/figure>\n<\/a><\/figure>\n