Reimagining Enterprise Media Lifecycle with AgenticAI – A Practical Guide

AI presents an immense opportunity to unlock value in the media life cycle for large advertisers.

The media function in a large brand is a multi step workflow that involves stakeholders across strategy, planning, analytics, operations and measurement contributing to the achievement of pre-agreed outcomes. While it is enticing to consider single AI models that claim to do everything – large enterprise advertisers will find them limiting, and the one size fits all approach is often not acceptable by marketers that look to build a unique brand equity with their target audience.

Agentic AI is the perfect solution for handling such complex media workflows. Unlike traditional AI, which typically operates within predefined rules, and generative AI, which primarily responds to a single prompt to create content, agentic AI is a proactive and goal-oriented system. It often leverages a large language model (LLM) as its “brain” for reasoning and planning, but its true power lies in its ability to execute multi-step workflows.

The term “agentic” refers to the system’s ability to exhibit agency – to have situational awareness, make reasoned decisions, and take actions to pursue its objectives. Key characteristics of Agentic AI include :

• Autonomy: The core defining feature is the ability to initiate and complete tasks without constant human oversight. Once given a high-level objective, an agentic system can break it down into smaller steps, execute them, and adapt its approach as needed.

• Reasoning and Planning: Agentic AI is capable of analyzing a problem, considering different options, and creating a step-by-step plan to achieve a goal. It can adjust this plan in real-time based on new information or unexpected obstacles.

• Adaptability: The system can learn from its environment and its own experiences. This allows it to adapt to changing conditions, correct its own errors, and improve its performance over time.

• Use of Apps and Tools: Agentic AI can call on and integrate with external apps, APIs, and other enterprise software systems to perform its tasks.

• Collaboration: A sophisticated agentic system can consist of multiple specialized AI agents that communicate and collaborate to achieve a shared objective. This is known as a multi-agent system, where each agent has a specific role that contributes to the final outcome.

Instead of a simple “human versus machine” paradigm, we could look at AI as an intelligent partner to automate routine tasks while empowering human teams to focus on high-level strategy, creative vision, analytical and ethical oversight. The media lifecycle—from strategy to measurement—can be broken down into specific functions. By mapping Agentic AI to the human roles traditionally responsible for each function, we can create the building blocks to orchestrate a powerful and efficient workflow.

At Shopalyst Labs, we are brewing Symphony – a multi-agent AI system to revolutionize the media workflow by moving beyond simple automation to a more collaborative, autonomous, and intelligent system. By using specialized AI agents each with a specific role, we orchestrate them to work together and achieve a shared goal. By leveraging custom configuration, brands get a chance to craft and configure their own unique AI powered media workflows to create competitive differentiation.

The power of a multi-agent system lies in their ability to communicate and collaborate. Think of it as a musical orchestra: the conductor not only knows the sequence of the music (the workflow) but also controls each musician (the service or resource), ensuring they play at the right time, with the right tempo, and in harmony. Listed below is an illustration of what the process would look like:

• Initiation: A human team provides a business brief to the Strategy Agent.

• Strategic Planning: The Strategy Agent analyzes the brief and communicates its findings and recommendations to the Media Planner Agent.

• Plan Development: The Media Planner Agent develops a detailed plan using data from the Bid Manager Agent and shares it with the Creative Agent for asset preparation and with the Activation Agent for technical setup. It also sends a draft plan to the human team for final review and approval.

• Creative Optimization: The Creative Agent works on variations and gets a pre-launch check from the Compliance Agent before sending the approved assets to the Activation Agent.

• Execution and Real-time Feedback: Once the human team approves the plan, the Activation Agent launches the campaigns. As data comes in, the Optimization Agent monitors performance and provides real-time feedback to the Activation Agent to adjust bids and budgets. It also sends anomaly alerts to the human team and the Reporting Agent.

• Reporting and Analysis: The Reporting Agent pulls data from the Activation and Optimization agents to generate live dashboards and regular reports for the human stakeholders. At the end of the campaign, it works with the Strategy Agent to analyze overall performance and gather insights for future campaigns.

• Continuous Improvement: The system has a feedback loop. The insights from the Reporting Agent are fed back into the Strategy Agent’s knowledge base, allowing it to “learn” from past campaigns and make better recommendations in the future.

• Increased Efficiency and Speed: The system can automate repetitive tasks like campaign setup, data pulling, and bid adjustments, freeing up human media buyers to focus on high-level strategy and creative development.

• Enhanced Performance: By constantly monitoring and optimizing campaigns in real-time, the system can achieve better performance and higher ROI than a human-led process.

• Proactive Issue Detection: The system can detect and address performance anomalies or compliance issues instantly, preventing potential financial losses or brand damage.

• Improved Transparency and Insights: The collaboration and logging of all agent actions provide a clear, auditable trail of decisions, giving a deeper understanding of campaign performance.

• Scalability: The modular nature of the multi-agent system means that you can easily add new agents or update existing ones to handle new channels or tools without having to redesign the entire workflow.