Agentic AI refers to the more advanced type of AI, aimed at handling complex tasks on behalf of its users and taking somewhat independent actions, i.e. with minimal oversight by humans.
What is Agentic AI?
From a technical perspective, agentic AI combines generative and traditional AI technologies to act in a more autonomous manner, often proactively, and adapt its actions based on previous experiences instead of a mere response to commands and prompts.
As commonly known, agentic AI solutions require large volumes of data from various sources (including both the first-party enterprise data elements and third-party app data, too) and utilize the so-called “multimodal reasoning” to better analyze a sophisticated task, hence compiling a potentially more efficient execution strategy, as well as planning it iteratively and continuously adjusting it in real time, based on the received results.
Spheres of Adoption
While less autonomous AI agents are commonly used in customer support to empower conversational chatbots and simplify daily operational routine (e.g. sort out and compile emails), the adoption of agentic AI unlocks revolutionary opportunities for various industry sectors, including supply chain management, healthcare, tourism, autonomous vehicle management, and many more.
In the digital advertising space, in particular, not only can agentic AI act as a full-fledged online ad campaign creator (potentially reducing ad agency roles), but also take the entire campaign planning and management to the next level, by being able to analyze, adjust and reallocate bids and budgets in a continued manner, based on the ongoing performance.
One of the vivid examples of agentic AI is Google DeepMind’s Project Astra (now available as the prototype), powered by Gemini 2.0, which can potentially transform and revolutionize the entire digital industry by pushing the boundaries beyond the seamless search & discovery with a so-to-speak “universal assistant”. However, the perspectives of its market-wide implementation depend on a variety of factors, including the wider adoption of specific hardware (i.e. smart glasses) and cross-platform interoperability, hence being unclear as of today.