The explosive growth of agentic AI frameworks and tools has created a paradox of choice for developers, leading to a landscape so dense and complex that it often paralyzes decision-making when it comes to building next-generation applications. Faced with a dizzying array of options, enterprise teams struggle to determine the right models and strategies for their specific needs. A new comprehensive framework presented by researchers from multiple institutions offers a beacon in this fog, categorizing agentic systems to provide a clear, practical guide. This reframes the conversation entirely, shifting it from a simple model-selection problem to a sophisticated architectural decision. It forces organizations to strategically consider where to allocate their training budgets, how to preserve modularity for future upgrades, and what critical trade-offs they are willing to accept between performance, cost, flexibility, and risk in their AI systems.
1. The Two Core Dimensions of AI Systems
At the heart of this new framework lies a fundamental division of the agentic landscape into two primary dimensions: agent adaptation and tool adaptation, each representing a distinct philosophy for enhancing an AI’s capabilities. Agent adaptation focuses on directly modifying the foundation model that serves as the system’s core intelligence. This is achieved by updating the agent’s internal parameters or policies through resource-intensive methods like fine-tuning or reinforcement learning. The objective is to rewire the model’s “brain” to better align its inherent reasoning and decision-making processes with specific, targeted tasks. This approach treats the AI model as a malleable entity that can be sculpted and refined to achieve a higher degree of specialization, effectively teaching it new skills from the inside out. This method promises maximum flexibility and performance but comes with significant computational and data-related costs, making it a high-stakes investment in a model’s intrinsic abilities.
In stark contrast, the tool adaptation strategy shifts the optimization focus from the agent itself to the external environment and the tools it interacts with. Instead of undertaking the expensive and complex process of retraining a large foundation model, developers concentrate on improving external components such as search retrievers, memory modules, or specialized sub-agents. Within this paradigm, the main agent remains “frozen,” its core parameters and learned knowledge untouched. This approach allows the overall system to evolve and improve its performance without incurring the massive computational overhead associated with retraining the central model. By enhancing the quality and relevance of the information and capabilities available to the agent, the system becomes more effective. This strategy is akin to giving a highly intelligent person better instruments and assistants rather than trying to fundamentally change how they think, offering a more cost-effective and modular path to performance gains.
2. Four Key Adaptation Strategies Explained
Diving deeper into these dimensions, the research identifies four distinct strategies that offer a more granular view of system design. The first agent-centric strategy, A1 or “Tool execution signaled,” involves optimizing an agent through direct, verifiable feedback from a tool’s operation. In this scenario, the agent learns by doing, receiving a clear success or failure signal based on its actions. For example, the DeepSeek-R1 model was trained using reinforcement learning where it received rewards for generating code that successfully executed in a sandboxed environment. This binary and objective feedback teaches the agent the precise “mechanics” of using a tool correctly, building strong, low-level competence in stable and verifiable domains like coding or SQL queries. The second strategy, A2 or “Agent output signaled,” trains the agent based on the quality of its final answer, irrespective of the intermediate steps or tool calls made. The Search-R1 agent, designed for multi-step information retrieval, exemplifies this by receiving a reward only if its concluding answer is correct. This implicitly forces the model to learn superior search and reasoning strategies to orchestrate various tools effectively, making A2 ideal for mastering complex, system-level workflows.
On the tool-centric side of the spectrum, the T1 strategy, or “Agent-agnostic,” involves training tools independently on broad datasets before they are “plugged in” to assist a frozen agent. A classic example is the dense retriever models used in Retrieval-Augmented Generation (RAG) systems. These retrievers are trained on generic search data and can be used by any powerful, frozen Large Language Model (LLM) to find relevant information, even though the tool was not designed specifically for that LLM. Conversely, the T2 strategy, known as “Agent-supervised,” creates a symbiotic relationship by training tools specifically to serve a particular frozen agent. The supervision signal for training the tool comes directly from the agent’s own output. The s3 framework demonstrates this by training a small “searcher” model that is rewarded based on whether a large, frozen “reasoner” LLM can correctly answer a question using the documents it retrieves. The tool effectively learns to anticipate and fill the specific knowledge gaps of its partner agent. Sophisticated AI systems often blend these approaches, perhaps using T1 retrievers for general search, T2 agents for adaptive filtering, and A1 agents for specialized execution within a single, orchestrated workflow.
3. Balancing Costs and Benefits
For enterprise decision-makers, navigating these strategies requires a careful evaluation of the trade-offs between cost, generalization, and modularity. The choice between agent and tool adaptation has profound financial and operational implications. Agent adaptation (A1/A2) offers unparalleled flexibility by directly reshaping the agent’s core logic, but the costs are substantial. The development of Search-R1, an A2 system, required training on a massive dataset of 170,000 examples, demanding significant compute resources and specialized data curation. While this initial investment is high, the resulting models can be much smaller and more efficient at inference time because their capabilities are internalized. In contrast, tool adaptation (T1/T2) presents a far more cost-effective path. The s3 system (T2) achieved comparable performance by training a lightweight searcher with only 2,400 examples—roughly 70 times less data than Search-R1. By optimizing the ecosystem around the agent rather than the agent itself, organizations can reach high performance levels with a fraction of the training cost. However, this efficiency comes with a trade-off, as these systems may incur higher overhead during inference due to the necessary coordination between the smaller tool and the larger core model.
Beyond the initial cost, the chosen strategy significantly impacts a system’s ability to generalize to new tasks and its long-term maintainability. Agent adaptation methods like A1 and A2 carry the risk of “overfitting,” a phenomenon where an agent becomes so highly specialized in one domain that it loses its broader, more general capabilities. The study highlighted this when Search-R1, despite excelling at its training tasks, struggled with a specialized medical question-answering dataset, achieving only 71.8% accuracy. This specialization is acceptable for dedicated, single-purpose agents but limits their versatility. Conversely, the s3 system (T2), which paired a trained tool with a general-purpose frozen agent, demonstrated superior generalization by achieving 76.6% accuracy on the same medical tasks. The frozen agent retained its vast world knowledge, while the specialized tool handled the specific retrieval mechanics. Tool adaptation also excels in modularity. T1 and T2 strategies enable “hot-swapping,” allowing components like a memory module or searcher to be upgraded without altering the core reasoning engine. A1 and A2 systems, however, are monolithic; teaching a fine-tuned agent a new skill can lead to “catastrophic forgetting,” where it degrades on previously learned abilities as its internal weights are overwritten.
4. A Strategic Framework for Enterprise Adoption
Based on these trade-offs, developers can approach agentic AI implementation as a progressive ladder, moving from low-risk, modular solutions toward high-investment, deep customization. The recommended starting point is T1, using agent-agnostic tools. This involves equipping a powerful, off-the-shelf frozen model, such as those from the Gemini or Claude series, with standard tools like a dense retriever or an MCP connector. This approach requires zero custom training, making it exceptionally low-cost and ideal for rapid prototyping and building general-purpose applications. For a vast majority of common tasks, this “low-hanging fruit” strategy can deliver impressive results with minimal upfront investment, providing a solid foundation for more complex systems. It allows teams to quickly validate concepts and deliver value without committing to a costly and time-consuming development cycle, making it the most practical entry point into the world of agentic AI.
Should the initial T1 implementation prove insufficient, with the agent struggling to effectively use generic tools, the next logical step is to adopt a T2, or agent-supervised, strategy. Crucially, this does not mean retraining the expensive main model. Instead, the focus shifts to training a small, specialized sub-agent—such as a searcher, filter, or memory manager—to preprocess, format, and deliver data in a way that is perfectly tailored to the main agent’s needs. This method is highly data-efficient and is particularly well-suited for applications involving proprietary enterprise data, where the nuances of the information are unique to the organization. By creating a custom tool that speaks the main agent’s language, systems can achieve significant performance gains, especially in high-volume, cost-sensitive environments. This targeted approach preserves the modularity of the system while addressing specific performance bottlenecks, offering a balanced compromise between customization and cost.
5. The Future Is a Smart Ecosystem
When an agent fundamentally fails at technical tasks, such as writing non-functional code or making incorrect API calls, it signals a deeper issue that tool adaptation alone cannot solve. In these cases, the agent’s core understanding of a tool’s “mechanics” must be rewired, which calls for the A1 strategy of tool execution signaled feedback. This approach is best reserved for creating specialists in highly verifiable domains like SQL, Python, or proprietary internal tools where correctness can be objectively measured. For example, a small model could be optimized specifically for an organization’s unique toolset and then be used as a T1 plugin for a larger, generalist model. The final and most resource-intensive strategy, A2 or agent output signaled, should be considered the “nuclear option.” Training a monolithic agent end-to-end should only be undertaken when the goal is to have it internalize complex, multi-step strategies and self-correction behaviors. This level of deep integration is rarely necessary for standard enterprise applications and requires a level of investment in data and computing that is prohibitive for most organizations. In reality, very few use cases justify the cost and complexity of training a custom, monolithic model from the ground up.
The evolution of the AI landscape made it clear that the dominant paradigm had shifted. The pursuit was no longer centered on building a single, gargantuan model that could do everything perfectly. Instead, the focus had pivoted toward the art of constructing a smart, efficient ecosystem of specialized tools and agents that revolve around a stable, powerful core. For most enterprises, the analysis concluded that the most effective and sustainable path to unlocking the power of agentic AI was not in the costly endeavor of building a bigger brain. Rather, the true potential was realized by providing the existing brain with a suite of better, more intelligent tools. This strategic move from monolithic intelligence to modular ecosystems represented a maturing of the industry, where architectural wisdom and pragmatic resource allocation had triumphed over the brute-force approach of simply scaling up model size.
