Imagine your marketing team equipped with AI specialists for every customer engagement challenge – where agents work like a well-coordinated team from analyzing customer data to creating compelling content, all while ensuring brand and legal compliance. This isn’t speculative fiction. It’s the emerging reality of agentic AI.
Agentic AI is fundamentally transforming enterprise marketing. As organizations evaluate strategies and capabilities, a deliberate, structured approach is essential. This process can be complex and challenging, underscoring the importance of adopting a conscientious approach to agentic AI from the outset, as it will significantly shape long-term outcomes.
Agentic AI approaches
Consider how a telecommunications company launching an international roaming campaign traditionally relies on AI for marketing content – often resulting in slow progress. Agentic AI changes that – marketing, analytics, creative, and compliance agents operate in parallel – each contributing their expertise toward a shared goal of educating customers about roaming benefits and driving plan-specific adoption. This is just one example organizations across industries are currently encountering.
Today, organizations face a strategic fork in the road:
- Prompt-first agentic engineering: Agents operate across workflows, guided by structured prompts and instructions.
- Workflow-first agentic engineering: Agents are embedded within orchestrated workflows, enabling predictable, auditable behavior and governance.
Both methods employ large-language models (LLMs) for agentic purposes but differ in how the models are applied. With workflow-first agentic engineering, agents are positioned within structured workflows, supporting predictable and auditable processes and guiding agent design and behavior control. In contrast, prompt-first agentic engineering underscores structured prompts, instructions, and actions to guide agents. While agentic workflows may be present, they are not central to the design – as agents function across workflows instead of being embedded within them. Prompt-first methods might result in fragmented customer experiences, data handling inconsistencies, and poor escalation paths. Workflow-first design mitigates these risks by embedding agents into enterprise logic and oversight.
Autonomy is another key consideration. Organizations evaluating agentic AI should review the levels of autonomy granted to agents. Highly autonomous agents may suit standardized or repetitive marketing tasks requiring limited human intervention. However, applying prompt-first methods to comprehensive marketing strategies needing integration with broader, cross-functional enterprise processes demands careful governance and orchestration.
Why a workflow-first agentic approach matters
Most AI systems today behave like black boxes – unpredictable planning, ad hoc execution. A workflow-first approach incorporates agentic AI reasoning during the design phase and shifts to predictable AI upon execution. This framework enables agents, applications, systems, and data to collaborate within governed workflows. Unlike approaches that deploy autonomous AI agents on top of workflows, this method ensures agent activities remain integrated and systematically managed. By embedding agentic capabilities into core processes, it proactively addresses challenges related to agent behavior and mitigates risks associated with the proliferation of disconnected or unpredictable agents. This approach blends analytical rigor (“left brain”) with creative flexibility (“right brain”), all within controlled boundaries that support adaptation and innovation without chaos.
Conversely, emphasizing autonomous reasoning via LLMs, which rely on structured prompts rather than embedded workflows, may result in complications. Prompt-first designs often struggle with governance, scalability, and enterprise alignment. These issues often arise from notable limitations in contemporary LLMs’ so-called "jagged intelligence," such as inadequate contextual comprehension, bias and ethical concerns, and insufficient transparency.
Present state of agentic marketing
Agentic marketing is an emerging sector currently focused on task-specific functions, such as content creation and campaign design. These capabilities are primarily driven by prompts that are highly sensitive to query phrasing, with minor adjustments often resulting in variable outcomes for both the primary task and its related marketing and enterprise processes. This is commonly referred to as “agent sprawl,” where agents can act unpredictably. The prevalence of agent washing muddies the waters further. Many organizations mistakenly attribute new agentic features to what are essentially rebranded versions of traditional AI-powered marketing technologies that lack substantive agentic functionality.
Despite the hype, “AI ROI remains elusive.” As agentic marketing continues to evolve, it is critical to look beyond immediate efficiency and productivity improvements associated with isolated or narrow marketing tasks. The next phase in agentic marketing signals collaborative agents operating within the enterprise, followed by agent-to-agent ecosystems spanning platforms. Achieving success in this dynamic field will require prioritizing a thoughtful design approach that enables agents to support creativity and exploration in marketing while integrating reliable workflows governed by comprehensive enterprise-level oversight and orchestration.
Agents of the future
In its “Emerging Tech: Avoid Agentic AI Failure: Build Success Using Right Use Cases” report, Gartner warns that “over 40% of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value, or inadequate risk controls.”
Achieving success in agentic marketing requires framing agents of today with a workflow-first approach that incorporates resiliency and adaptability to rapid market changes. Establishing a unified, goal-oriented agentic marketing strategy – where design is closely aligned with enterprise-level execution – depends on this solid foundation. Further reinforced by governed autonomy, architectural openness, interoperability, and flexible deployment, organizations can adjust and balance diverse internal objectives with external ecosystems. Empowering agents of the future starts with understanding the differences in agentic AI approaches – and choosing the right path forward.
Want to learn more about how agentic AI can transform customer interactions? Watch this video to see how Pega Customer Decision Hub™ redefines personalization that drives engagement and builds lasting relationships.