Agentic AI moves from possibility to pragmatism
Agentic artificial intelligence (AI) represents the next evolutionary leap in enterprise automation by moving beyond Generative AI’s (gen AI’s) language capabilities to enable goal-driven, autonomous systems that can perceive, reason, decide, and act in dynamic environments.
While traditional automation relies on static rules and linear scripts to execute predefined tasks, agentic AI relies on dynamic context interpretation, advanced reasoning, and adaptive planning to pursue outcomes with minimal human intervention.
While the possibilities are transformative, ranging from self-operating customer service agents to agents that autonomously reconcile financial data and monitor compliance, the path to adoption is far from uniform. As interest surges, enterprise leaders are now facing a pivotal question: How should we operate agentic AI inside the organization?
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Agentic AI products are evolving along two tracks – multi-domain agentic products and single-domain agentic products. Within multi-domain products, three categories come to the fore, each representing a different blend of control, customization, and integration effort. These are:
- Developer tools and frameworks
- Agent builder platforms
- Pre-built / customizable agents
Single-domain agentic products, on the other hand, are focused point solutions optimized for narrow functional contexts such as coding, healthcare documentation, or personal productivity.
Exhibit 1 depicts the various agentic AI product categories available to enterprises.
This blog decodes the adoption paths, maps them to the buy vs. build decision continuum, and helps enterprises make informed decisions based on their maturity, use cases, and strategic intent.
The build to buy spectrum: mapping enterprise choices
Multi-domain products are designed to operate across varied enterprise workflows and functional areas, enabling broader automation and orchestration capabilities. These products are more scalable, configurable, and adaptable to complex environments.
There are 3 categories of multi-domain agentic products available today:
- Developer tools and frameworks
Build-centric products composed of Application Programming Interfaces (APIs), Software Development Kits (SDKs), and prebuilt libraries that offer foundational components for creating AI agents. These enable deep customization and seamless integration into enterprise environments - Agent builder platforms
Positioned as low-code or no-code platforms which enable users (often business users with limited technical knowledge) to assemble agent workflows using pre-configured blocks, connectors, and logic. They strike a balance between usability and customization, making them ideal for line-of-business teams with limited technical support - Pre-built / customizable agents
Packaged, ready-to-use agents designed for specific tasks or industries (e.g., customer support, claims triage) that offer quick time-to-value with limited configuration
Together, these present a spectrum of adoption choices, each offering a different balance of customization, speed, and scalability. Exhibit 2 highlights the buy vs build matrix to aid decision making.
Key decision factors influencing the choice of agentic product category
Selecting the right agentic AI product category is not purely a technical decision, rather it is a strategic one, shaped by enterprise priorities, capabilities, and constraints. Key factors that influence this choice include:
- Deployment speed: Build-heavy options, such as developer tools and frameworks, typically demand longer implementation timelines due to deep integration and customization needs. As we move toward agent builder platforms and eventually to pre-built agents, deployment becomes progressively faster, making them well-suited for rapid experimentation and immediate operational impact
- Need of technical skills Deploying products with high customization levels requires corresponding technical depth. Enterprises need mature AI/Machine Learning (ML) teams or strong engineering partners to effectively leverage developer frameworks. Moving towards the other end reduces talent burden, enabling business-led or hybrid teams to build and manage agents with minimal technical intervention.
- Level of customization: The greater the need for domain specificity, regulatory control, or adaptability, the stronger the case for opting for build-oriented products. In contrast, standardized or repeatable processes are often better served by low-code builder platforms or off-the-shelf agents, which trade off depth for speed and simplicity.
- Ease of integration: Developer tools and frameworks require robust enterprise architecture for orchestration, memory management, security, and compliance. Agent builder platforms reduce this load through abstracted environments, and prebuilt agents typically integrate with minimal effort, though with limited flexibility and extensibility.
- Entry level affordability: Usage of developer tools and frameworks incurs higher upfront cost due to cost of set-up, architecture design, infrastructure, integrations and sourcing of technical expertise. Comparatively, pre-built agents have a low initial investment as they are ready to use with their out-of-the-box working, minimal set-up and low configuration and customization needs.
- Scalability: Developer tools and frameworks offer unparalleled scalability, aligning with the demands of large enterprises. While not reaching the same level, agent builders do provide a degree of scalability that supports enterprise growth without requiring infrastructure re-engineering. In contrast, pre-built agents offer functional scalability along pre-defined dimensions but often lack the elasticity and customization necessary for effective cross-domain expansion.
By evaluating these dimensions up front, enterprises can better align their agentic AI strategy with long-term transformation goals. Exhibit 3 helps enterprises identify the best fit across the dimensions.
To further support strategic decision-making, it is essential that the evaluation is not limited to functionality but also encompasses how well the products align with enterprise maturity, implementation effort, and the desired level of control.
Agentic readiness: What enterprises often overlook
Despite growing market options, many enterprises underestimate the level of readiness required for agentic AI adoption. Choosing the right product category is only part of the equation, enterprises must also address the four core “debts” that often hinder adoption: process debt, technology debt, skill debt, and data debt. These represent critical gaps that, if left unresolved, can limit scalability, increase risk, and delay value realization. Exhibit 4 outlines these key debt areas that enterprises must overcome to operationalize agentic AI effectively.
- Process debt: Intelligent agents must align with real-world workflows and not the other way around. Many enterprises overlook the need to redesign, map, or standardize processes to enable autonomous execution. Without this foundation, agents often operate in isolation, fail to scale, or end up duplicating existing efforts
- Technology debt: As products shift from build-heavy to plug-and-play, the underlying technical burden changes. Developer-centric models require robust orchestration, runtime control, and memory handling. Simpler options reduce that load but still demand alignment with enterprise systems, APIs, and deployment environments
- Skill debt: Each product category places varying demands on internal talent. At one end, organizations need engineering and ML expertise to build and manage agents. At the other, they must still ensure domain-informed configuration and proper validation. A common oversight is assuming agentic systems can be “configured and forgotten” without the right governance and cross-functional skill mix, performance and adoption suffer
- Data debt: Agentic AI relies on access to clean, contextual, and timely data to make decisions. As solutions scale across functions or integrate Large Language Models (LLMs), data quality, access rights, and privacy enforcement become critical. While narrow-domain agents may operate on localized datasets, multi-domain deployments require consistent data orchestration across silos
Taken together, these considerations often determine whether agentic AI delivers scalable value or stalls at the proof-of-concept stage. Enterprises that proactively assess and strengthen their readiness across process, technology, skill, and data dimensions will be far better positioned to translate choices into meaningful outcomes.
Outlook:
As the agentic AI market continues to evolve, enterprises must evaluate the options available and zero in on the best fit based on their unique requirements. While choosing the right product category is important, it alone will not guarantee success. Enterprises must adopt an outcome-first mindset, anchoring adoption efforts to high-impact business goals, based on contextual fit rather than familiarity, and planning for modularity so that today’s decisions can scale with tomorrow’s needs.
Agentic AI is not just about deploying intelligent agents; it is about laying the foundation for enterprise autonomy and creating organizations that can think, learn, and act with intelligence at scale.
If you enjoyed reading this blog, check out Agentic AI: True Autonomy Or Task-based Hyperautomation? | Blog – Everest Group, which delves deeper into other topics regarding Agentic AI.
To learn more about this topic, as well as anything else relating to Everest Group, please contact Ruchin Dwivedi ([email protected]), Vershita Srivastava ([email protected]) and Vaibhav Bansal ([email protected]__)
