We enthusiastically observe the growing fascination with AI Agents. However, amidst this effervescence, a crucial question arises: Are we building AI Agents out of a genuine strategic need, or simply due to the inertia of a trend? It’s imperative to address this to avoid costly mistakes in the medium and long term.
The excitement about agents’ capabilities—their autonomy, reasoning ability, and interaction with the environment—is understandable. But the mistake lies in indiscriminate adoption, without a solid strategic foundation. Building an agentic solution “just because” can lead to:
- Resource dispersion: Significant investments in development, infrastructure, and maintenance without a clear return on investment (ROI).
- Unnecessary complexity: An agent might solve a simple problem in an overly complex way when a more direct solution would be more efficient.
- Lack of adoption and scalability: If the solution doesn’t solve a real business problem and doesn’t integrate into an existing workflow, it’s likely to fall into disuse.
- Technical debt: The accumulation of isolated, difficult-to-maintain systems that ultimately reduce agility and innovation capacity.
To make the most of AI Agents, it’s fundamental that their implementation is part of a comprehensive and coordinated strategy. It’s not about building isolated agents, but about integrating them into a data and process ecosystem. The main characteristics for achieving this are:
- Clear definition of the problem and business value: Before thinking about technology, we must understand what business problem we want to solve and what tangible value the agent will bring. Will it improve operational efficiency? Increase customer satisfaction? Discover new business opportunities?
- Alignment with data strategy: AI agents thrive on data. It’s crucial to have a robust data strategy that ensures the availability, quality, and accessibility of the necessary information. This aligns with effective data management, ensuring the data infrastructure is appropriate.
- Integration with existing systems: An agent cannot operate in a vacuum. It must be able to interact seamlessly with enterprise systems, whether CRM, ERP, or internal databases. A robust data platform is vital here to build or refine the technical infrastructure.
- Iterative and value-centric approach: Instead of large, monolithic projects, we advocate for an agile approach, where prototypes are built and tested in real-world scenarios, learning and refining with each iteration.
- Governance and ethics: Agent autonomy requires clear governance and ethical considerations. How will their decisions be audited? What biases might exist?
Multi-Agent Platforms: Synergy for Impact
In certain scenarios, the true power of AI lies not in a solitary agent, but in the coordinated interaction of multiple agents.
A multi-agent platform (also known as a multi-agent system, or MAS) is a computerized system comprised of multiple interacting intelligent agents. These systems are designed to solve problems that are too complex, large-scale, or decentralized for a single, individual agent or a monolithic system to handle effectively.
Multi-agent platforms are appropriate when:
- The problem is complex and requires diverse specializations: For example, in a supply chain, one agent could optimize routes, another manage inventories, and a third predict demand. The collaboration of these “teams” of agents can achieve optimization that an individual agent couldn’t.
- There are interdependencies between tasks: The actions of one agent directly affect those of another, demanding constant coordination and communication.
- Resilience and redundancy are sought: If one agent fails, others can take over its responsibilities or compensate for its absence.
- Modular scalability is desired: Agents can be added or removed as needed, without restructuring the entire system.
The benefits of a multi-agent model are significant:
- Increased efficiency and performance: Specialization and collaboration allow for more effective tackling of complex problems.
- Flexibility and adaptability: Multi-agent systems can adapt to changes in the environment or business requirements more dynamically.
- Robustness: Task distribution reduces the risk of a single point of failure.
- Collective learning capacity: Agents can learn from interactions among themselves and with the environment, improving the overall system performance.
In essence, building AI Agents shouldn’t be a race driven by novelty, but a carefully planned strategic investment. By integrating a vision of business value, a solid data strategy, and coordinated implementation, companies can unlock the true transformative potential of artificial intelligence, building not just agents, but intelligent solutions that drive long-term growth and innovation.
Thinking in your own AI Agents strategy? Let’s talk now! hello@keepler.io
CMO at Keepler. "My experience is focused on corporate communications and B2B marketing in the technology sector. I work to position Keepler as a leading company in the field of advanced data analytics. I also work on a thousand other things to make Keepler a top company to work for."
0 Comments