Chapter 67: The Specialization Trend |
Summary |
The era of the monolithic general-purpose language model as the sole solution for enterprise AI is drawing to a close. Across finance, healthcare, and industrial operations, organizations are discovering that the largest general model, however impressive its benchmarks, often lacks the grounding, traceability, and domain precision required for consequential decisions. The emerging consensus points toward specialized models and agents: systems trained on or tightly coupled to domain-specific data and semantics. LSEG's Deep Research is grounded in licensed financial data with full provenance. TrustedMDT's agents are purpose-built for oncology tasks, from tumour staging to guideline-driven treatment planning. Via Automation's Via Co-Pilot is trained on industrial sensor data to turn predictive maintenance from a black box into a trusted collaborator. In manufacturing, frameworks like CoLMAgent and the 'Synergy of Giants and Specialists' architecture demonstrate that the path forward is not larger general models but precisely grounded specialized agents working in orchestrated collaboration. |
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1. The Limits of General-Purpose Models in Specialized Domains |
1.1 The Hallucination Problem in High-Stakes Contexts |
General-purpose large language models are remarkable engines of language. They can summarize, translate, reason, and generate with a fluency that seemed impossible a decade ago. Yet when deployed in domains where factual precision carries consequences, their fundamental architecture reveals a critical weakness: they optimize for plausible continuation, not verified truth. |
In financial services, a hallucinated revenue figure or a misattributed earnings estimate can mislead an investment committee. In oncology, a hallucinated staging classification or an incorrect treatment recommendation can affect patient survival. In manufacturing, a hallucinated diagnosis of equipment degradation can lead to unnecessary downtime or, worse, a missed failure that shuts down a production line. |
Research comparing domain-tuned and general models has begun to quantify this gap. A study evaluating fine-tuned GPT-4o against general models including Grok-3, Gemini, and DeepSeek on gravitational wave literature found that the domain-tuned model achieved the highest factual accuracy score, while general models often produced plausible-sounding but imprecise answers. The difference was not dramatic in every dimension, but in a domain where a decimal place or a confidence interval matters, marginal gains in accuracy compound into meaningful reliability. |
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1.2 The Grounding Gap |
Grounding refers to the connection between a model's outputs and verifiable sources of truth. A general model, trained on the open web, has no privileged access to the specific datasets that govern a professional domain. It does not know which version of a financial filing is current, which clinical guideline was updated last month, or which sensor reading indicates imminent bearing failure. |
LSEG's approach to Deep Research illustrates the alternative. Rather than relying on whatever financial information the model absorbed during training, Deep Research is explicitly grounded in LSEG's licensed datasets: end-of-day prices, IBES estimates, WorldScope fundamentals, Reuters news, and SEC filings, among others. Every output includes provenance: which data sources were used, what assumptions were made, and how the reasoning proceeded from input to conclusion. As LSEG's Group Head of Workflows noted, the goal is that clients 'can ask the most complex questions and trust the answer---grounded in LSEG data, with reasoning they can trace back to the source.' |
In manufacturing, the grounding problem takes a different form. A general model has never seen the vibration signatures of a specific bearing on a specific machine in a specific factory. It cannot distinguish normal operational variation from incipient failure without access to the sensor data that encodes that machine's behavior over time. Via Co-Pilot addresses this by training machine learning models directly on industrial sensor data, enabling diagnostics that are 'easily explainable, interpretable and highlight root causes, such as bearing wear or lubrication breakdown.' |
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1.3 The Traceability Imperative |
In regulated industries, the ability to explain a decision is not optional. Financial institutions must justify investment recommendations to clients and regulators. Healthcare providers must document the clinical reasoning behind treatment choices. Manufacturers must trace the chain of evidence that led to a maintenance action. |
General-purpose models, by their nature, produce outputs whose internal reasoning is opaque. Even when they show 'chain of thought,' that reasoning may not correspond to any verifiable process. Specialized agents, by contrast, can be designed with traceability as a first-class requirement. TrustedMDT's architecture ensures that each agent's output can be cross-checked against patient history and clinical guidelines, reducing the risk of 'guessing.' The system is explicitly designed so that 'the human remains the final decision-maker,' with the AI serving as a traceable collaborator rather than an autonomous oracle. |
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2. Specialized Agents in Finance: The LSEG Deep Research Case |
2.1 Architecture and Design Philosophy |
LSEG's Deep Research represents one of the clearest examples of the specialization trend in a commercial context. Rather than building a general assistant that happens to know about finance, LSEG built a research agent whose entire architecture is oriented around the structure and semantics of financial data. |
The system orchestrates workflows across LSEG's content universe, dynamically coordinating queries across structured datasets (prices, estimates, fundamentals) and unstructured content (news, transcripts, filings). When an analyst asks a complex question---say, how a change in interest rate expectations might affect a particular sector---Deep Research does not simply generate text from its training distribution. It queries the relevant datasets, retrieves current information, and synthesizes an answer whose provenance is documented at each step. |
2.2 Practical Applications Across Financial Workflows |
The applications span the full range of investment workflows. Portfolio managers can generate insights for idea generation and scenario modelling. Analysts can test hypotheses by exploring multiple data sources including filings, transcripts, and presentations. Investment teams can transform fragmented data into visualized, transparent insights that move from question to analysis faster than manual workflows allow. |
For risk teams and trading desks, Deep Research addresses the question of 'what changed' when markets move. It consolidates relevant market context and licensed news signals into a single report that separates confirmed information from speculation and highlights watchpoints for the next 24 to 72 hours. For macro and multi-asset decision-makers, it structures scenario sets and maps potential cross-asset implications with transparent logic that teams can pressure-test. |
2.3 The Role of Provenance and Auditability |
What distinguishes Deep Research from a general model with financial knowledge is not the sophistication of its language generation but the rigor of its evidentiary chain. Every output includes data sources, clear provenance of inputs and assumptions, and explainable AI workflows that enable users to understand how outputs are generated. This supports institutional-grade research standards and gives users confidence in the insights they act on. |
The system also supports what LSEG calls 'interactive artefacts': tables that can be extended with additional peers or metrics, charts that can be regenerated as assumptions change, all within the workspace where the user already operates. This integration into existing workflows, rather than a separate chatbot interface, reflects the recognition that specialized agents succeed when they serve the workflow rather than disrupt it. |
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3. Specialized Agents in Healthcare: TrustedMDT and Oncology |
3.1 The Clinical Need |
Multidisciplinary Tumour Board meetings are the gold standard for cancer treatment planning in the United Kingdom, convening radiologists, pathologists, surgeons, and oncologists to review diagnostic results and formulate recommendations. Yet rising caseloads strain expert capacity. A Cancer Research UK review found that teams often have less than two minutes of discussion time per patient, and critical information gaps lead to postponements in 7 percent of cases. These constraints produce treatment delays, missed research opportunities, and clinician burnout. |
3.2 TrustedMDT's Multi-Agent Architecture |
TrustedMDT, developed at the University of Oxford and piloted in collaboration with Microsoft, comprises three specialized agents working in concert. The Clinical Summarisation Agent analyzes electronic health records, including radiology, pathology, and biomarker tests, to produce concise, tumour-specific summaries. The Cancer Staging Agent applies international standards (AJCC/UICC) to determine disease stage. The Treatment Planning Agent drafts evidence-based recommendations aligned with professional guidelines. |
The architecture is explicitly hierarchical: each agent contains a dedicated team of sub-agents grounded in specific data with access to tools. As the lead investigator explained, 'Because standard chatbots struggle with the high-stakes complexity of oncology, we developed a hierarchical multi-agent system. This granular approach reduces the risk of 'guessing' because the system is required to reason through guidelines and explicitly cross-check its work against the patient's history.' |
3.3 Integration and Human Oversight |
A critical design decision was to embed the agents directly into Microsoft Teams, the environment clinicians already use for tumour boards, through the Microsoft Healthcare Agent Orchestrator. This positions the AI as a 'digital collaborator' rather than a separate system requiring additional cognitive overhead. Clinicians can provide new information in real-time, probe the rationale of recommendations, and maintain final decision authority. |
The evaluation protocol reflects the rigor appropriate to clinical deployment. Phase I validates the tool using anonymous cancer cases, benchmarking AI outputs against expert decisions. Phase II deploys the system in simulated tumour boards with clinicians to assess user experience and workflow integration. The primary endpoint is the rate of clinically significant errors, judged by clinicians as having potential to alter management. |
3.4 Beyond Treatment Planning: Autonomous EHR Agents |
The specialization trend in healthcare extends beyond tumour boards. RadOnc-GPT, an autonomous LLM agent for real-time patient outcomes labeling, demonstrates how specialized agents can navigate structured clinical data systems. Unlike general RAG pipelines that retrieve document chunks via vector similarity, RadOnc-GPT uses targeted, whitelisted functions to retrieve specific data types from Epic---clinical notes, radiology reports, pathology results---based on the fact that patient data, though unstructured, is systematically organized and indexed. This function-based approach reduces the volume of data returned per query and enables more precise reasoning over patient histories. |
At a more experimental frontier, systems like MIRA are being tested on simulated patient cases drawn from critical care databases, performing at or above physician level on measures including diagnostic accuracy and treatment quality in controlled settings. The researchers emphasize that prospective, real-world studies are needed to establish generalizability and safety, but the trajectory is clear: specialized agents are moving from assistive tools toward autonomous clinical functions, always under human oversight. |
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4. Specialized Agents in Manufacturing: Via Co-Pilot and Beyond |
4.1 The Economic Imperative |
Unplanned equipment downtime costs manufacturers more than 1.5 trillion dollars globally per year, a figure that grows as production systems become more complex and interdependent. The cost of a single hour of downtime has surged by 50 percent in just two years. Predictive maintenance has long promised to address this, but traditional approaches often produced 'black box' predictions that operators did not trust and could not act upon effectively. |
4.2 Via Co-Pilot: From Black Box to Trusted Collaborator |
Via Automation's Via Co-Pilot represents a deliberate shift in how predictive maintenance is presented and used. Rather than delivering opaque alerts, Via Co-Pilot provides AI-driven insights for equipment degradation using machine learning models trained on sensor data. Its diagnostics are designed to be 'easily explainable, interpretable and highlight root causes, such as bearing wear or lubrication breakdown.' This explicability transforms the system from a source of anxiety ('the algorithm says something is wrong') into a decision-support tool that engineers can evaluate, validate, and act upon. |
The system enables collaborative workflows for engineers, operators, and managers to interact with AI that delivers recommendations, validates insights, and triggers automated work orders. It also incorporates feedback loops that improve model accuracy over time and adapt to new operating conditions. Early results show reductions in unplanned downtime of 30 to 40 percent and maintenance cost reductions of 15 to 20 percent, with asset life extended by approximately 25 percent. |
4.3 The Broader Manufacturing Specialization Landscape |
Via Co-Pilot is one example of a broader pattern in industrial AI. Research frameworks like CoLMAgent propose collaborative systems that integrate large models for semantic understanding with small, specialized models for process-specific reasoning in scenarios such as aircraft engine maintenance and rolling mill gearbox diagnostics. The 'Synergy of Giants and Specialists' framework decomposes manufacturing process planning into stages where a large model performs multimodal perception and a specialized reasoning engine---grounded in manufacturing knowledge graphs---performs interpretable, traceable decision-making. |
What these approaches share is the recognition that the general model's role is not to know everything but to translate and orchestrate, while specialized components handle the domain-specific reasoning that requires precision. A general model can understand a human operator's natural-language query about a threading machine. It cannot, by itself, determine the optimal cutting parameters based on the machine's vibration history, material properties, and tool wear state. That requires a specialized model trained on that domain. |
4.4 Grounding in Industrial Sensor Semantics |
The grounding problem in manufacturing is fundamentally about sensor data. A bearing's vibration signature, a motor's current draw, a thermal profile over time---these are the 'language' of industrial equipment, and general models have no native fluency in them. Specialized models trained on sensor data learn to recognize the patterns that precede failure, the subtle shifts that distinguish normal wear from incipient breakdown. |
Via Co-Pilot's training on industrial sensor data enables it to make these distinctions and explain them in terms operators understand. This is not a general capability scaled up; it is a specialized capability built from the ground up for a specific class of problems. |
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5. The Architecture of Specialization: How Grounded Agents Are Built |
5.1 Domain-Specific Data as the Foundation |
The first principle of specialized agents is that they are built on data that general models do not have. LSEG's Deep Research is grounded in licensed financial datasets with governed access and clear provenance. TrustedMDT is grounded in electronic health records, clinical guidelines, and oncology standards. Via Co-Pilot is grounded in industrial sensor streams from specific equipment. |
This grounding is not merely a matter of fine-tuning a general model on domain data. It involves architecting the system so that its reasoning is constrained by, and traceable to, that data. A general model fine-tuned on medical text can still hallucinate a drug interaction. A specialized agent that retrieves drug interaction data from a verified database and cross-checks it against the patient's current medications has a structurally different reliability profile. |
5.2 Tool Use and Function Calling |
Specialized agents extend their grounding through tool use. RadOnc-GPT calls whitelisted functions to retrieve patient data from Epic, search PubMed, query clinical trial databases, and compute proton range data. LSEG's Deep Research orchestrates queries across structured and unstructured datasets through a managed workflow. TrustedMDT's agents access patient records, staging algorithms, and treatment guidelines through defined interfaces. |
This function-calling architecture provides a crucial benefit: the agent's access to information is bounded by the tools it is given. A general model can generate anything from its training distribution. A specialized agent with a curated toolset operates within a defined knowledge domain, reducing the surface area for hallucination. |
5.3 Orchestration and Collaboration |
Many of the most compelling specialized systems are not single agents but orchestrations of multiple specialized components. TrustedMDT coordinates summarization, staging, and treatment planning agents. Financial multi-agent systems decompose analysis into technical, sentiment, fundamental, and risk agents. Manufacturing frameworks coordinate perception, knowledge representation, and reasoning modules. |
This orchestration mirrors the structure of professional expertise. A tumour board does not consist of a single physician who knows everything; it consists of specialists who contribute their particular expertise to a collective decision. The specialized AI architecture reflects and augments this division of cognitive labor. |
5.4 Human-in-the-Loop by Design |
A recurring theme across these systems is the explicit placement of human judgment in the decision loop. TrustedMDT's outputs are never directly executed; clinicians review and can override every recommendation. Via Co-Pilot's recommendations are presented for validation before triggering work orders. LSEG's Deep Research produces research artifacts for analyst review, not autonomous trading decisions. |
This is not merely a regulatory necessity but a design philosophy. Specialized agents are positioned as amplifiers of human expertise, not replacements for it. The human provides context, judgment, and accountability that the agent cannot. The agent provides speed, consistency, and the ability to process information at a scale the human cannot. |
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6. Implications for Manufacturing and Industrial Operations |
6.1 From General Automation to Cognitive Collaboration |
The specialization trend has profound implications for how manufacturers think about automation. Traditional industrial automation executes pre-programmed sequences: if sensor reads X, open valve Y. This is deterministic and reliable but brittle---it cannot handle novel situations or adapt to changing conditions without reprogramming. |
Specialized AI agents introduce a different kind of automation: cognitive collaboration. The agent understands context, reasons about goals, and proposes actions. It can handle variability that would confound a rules-based system. But unlike a general model, its reasoning is grounded in the specific domain of the machine or process it serves. |
CoLMAgent's framework illustrates this shift. A large model interprets the operator's intent and decomposes it into structured subtasks. Specialized small models execute those subtasks---diagnosing a fault, retrieving a maintenance procedure, calculating optimal parameters. The results feed back to the large model for synthesis into a coherent response. The operator interacts in natural language, but the reasoning is grounded in industrial domain knowledge and validated against operational data. |
6.2 The Traceability Advantage in Regulated Industries |
Manufacturing, like finance and healthcare, operates under regulatory and quality regimes that demand traceability. When a maintenance action is taken, the reasoning must be documented. When a production parameter is adjusted, the basis must be recorded. General models, with their opaque generation, struggle to meet these requirements. |
Specialized agents, particularly those using knowledge graphs and interpretable reasoning mechanisms, can produce step-by-step decision paths that resolve the 'black-box' problem of conventional data-driven methods. A maintenance recommendation can be traced to the sensor readings, the model's diagnostic logic, and the historical cases that informed it. This traceability is not an add-on; it is baked into the architecture. |
6.3 The Path Forward for Industrial AI |
The future of AI in manufacturing is not a single, all-knowing model that manages every aspect of operations. It is a constellation of specialized agents, each grounded in a specific domain---a machine, a process, a supply chain function---orchestrated by a coordination layer that routes requests to the appropriate specialist. |
This architecture mirrors the structure of a well-run factory: specialists at each station, a supervisor coordinating the flow, information flowing up and decisions flowing down. The difference is that the specialists are now AI agents, and the supervisor is a human augmented by AI orchestration. |
The economic case is compelling. Unplanned downtime reductions of 30 to 40 percent, maintenance cost reductions of 15 to 20 percent, and asset life extensions of 25 percent are not marginal improvements. They represent a step change in operational efficiency that general-purpose models, for all their eloquence, have not delivered in industrial settings. |
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7. The Specialization Trend in Broader Context |
7.1 Why Now |
The shift toward specialized agents is enabled by several converging developments. Foundation models have become capable enough to serve as semantic translators and orchestrators, understanding natural language intent and decomposing it into structured tasks. Small models and domain-specific architectures have matured to the point where they can deliver reliable, interpretable results within their domains. Tool-use frameworks and orchestration protocols have emerged to connect these components into coherent systems. |
At the same time, the limitations of general models in high-stakes domains have become more apparent as organizations move from experimentation to deployment. The gap between a demo that impresses and a system that can be trusted with consequential decisions is bridged by grounding, traceability, and domain precision. |
7.2 The Economics of Specialization |
From a purely economic perspective, specialized agents often make more sense than scaling general models indefinitely. A small model trained on a specific machine's sensor data can run on edge hardware, providing real-time inference without network latency or cloud costs. A specialized financial agent grounded in licensed data delivers more value per token than a general model attempting to reason about markets from training knowledge alone. |
The economics also favor specialization in terms of accuracy per unit of compute. Research on medical question answering found that lightweight, domain-adapted models achieved higher proportions of clinically sufficient and good responses than general-purpose counterparts, which exhibited greater variability between highly accurate and critically lacking outputs. In domains where consistency matters as much as peak performance, specialization delivers better reliability. |
7.3 The Orchestration Layer |
As specialized agents proliferate, the orchestration layer that coordinates them becomes increasingly important. This is where general models retain a role: not as the source of domain truth, but as the interface between human intent and specialized execution. |
A user asks a question in natural language. The orchestrator understands the intent, identifies which specialized agents can address it, routes the query or queries, collects results, and synthesizes a response. The general model provides flexibility and natural interaction; the specialized agents provide grounded, reliable answers. |
This division of labor is emerging across domains. In finance, a manager agent coordinates Text-to-SQL, unstructured data, ML/SHAP, and visualization agents to support equity analysis. In manufacturing, a cognitive core coordinates small models for process control and diagnostics. In healthcare, a hierarchical architecture coordinates summarization, staging, and planning agents. |
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8. Challenges and Open Questions |
8.1 Integration Complexity |
Building specialized agent systems is more complex than deploying a single model. Each agent must be developed, grounded in its domain data, integrated with tools and data sources, and validated for its specific task. Orchestration introduces additional complexity: agents must communicate, share context, and handle failures gracefully. The integration challenge is real, and organizations that underestimate it will struggle. |
8.2 Evaluation and Validation |
How do you validate a system whose value comes from specialized groundingTraditional benchmarks for language models measure general capabilities. Specialized agents require domain-specific evaluation: clinical accuracy against expert judgment, financial reasoning against market outcomes, industrial diagnostics against actual equipment failures. TrustedMDT's two-phase protocol---automated and expert evaluation followed by simulation-based pilot---provides a template for rigorous validation, but such protocols require domain experts, time, and institutional commitment. |
8.3 The Risk of Over-Specialization |
A specialized agent grounded in one domain may fail when confronted with edge cases that require broader knowledge. A manufacturing agent trained on one type of machine may not generalize to another. A clinical agent specialized for one cancer type may not serve a patient with multiple conditions. The challenge is to build specialization without brittleness, grounding without narrowness. |
One response is to maintain a general model in the orchestration layer, capable of recognizing when a query falls outside the specialized agents' domains and handling it appropriately---perhaps by routing to a human or by engaging a different agent. The architecture must be designed for graceful degradation, not just peak performance. |
8.4 Data Access and Governance |
Specialized agents depend on data: financial records, clinical notes, sensor streams. Accessing and governing that data raises familiar challenges. In healthcare, patient data is protected by privacy regulations that constrain how it can be used for model training. In finance, licensed data carries usage restrictions. In manufacturing, sensor data may be proprietary and sensitive to competitive exposure. |
Solutions like LSEG's governed access model, where users can access AI-powered insights only for data they are entitled to, provide one pattern. Federated learning approaches that train models without centralizing data offer another. The specialization trend will succeed only if data governance keeps pace with data use. |
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9. Detailed Summary and Future Trajectories |
9.1 The Specialization Thesis |
The central argument of this chapter is that the future of enterprise AI belongs not to the largest general model but to the most precisely grounded specialized agents. This thesis is supported by evidence from three domains: |
In finance, LSEG's Deep Research demonstrates that grounding in licensed data, with full provenance and auditability, produces research outputs that professionals can trust and act upon. The system's value comes not from the size of its underlying model but from the quality of its data and the rigor of its evidence chain. |
In healthcare, TrustedMDT shows that multi-agent architectures with domain-specific grounding---clinical records, staging standards, treatment guidelines---can support high-stakes decisions while keeping human judgment at the center. The system's design explicitly rejects the general chatbot approach in favor of specialized agents that reason through guidelines and cross-check against patient history. |
In manufacturing, Via Co-Pilot and related frameworks demonstrate that grounding in sensor data and interpretable diagnostics can transform predictive maintenance from a black box into a trusted operational tool. The economic results---significant reductions in downtime and maintenance costs---validate the specialized approach. |
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9.2 The Architecture of the Future |
The specialized agent architecture has several recurring features: |
Grounding in domain data: Agents are built on data that general models do not have access to, whether licensed financial datasets, clinical records, or industrial sensor streams. This grounding is structural, not incidental. |
Traceability by design: Outputs include provenance, reasoning paths, and evidence chains that support verification and audit. The system can explain not just what it recommends but why. |
Tool use and function calling: Agents access information and execute actions through defined interfaces, bounded by the tools they are given and the permissions they hold. |
Orchestration and collaboration: Multiple specialized agents work together, coordinated by an orchestration layer that may include a general model serving as semantic translator and router. |
Human-in-the-loop: Humans review, validate, and retain decision authority. Agents amplify expertise rather than replacing it. |
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9.3 The Evolving Role of General Models |
General models are not disappearing from the enterprise stack. Their role is shifting from sole provider of intelligence to orchestrator and translator. They excel at understanding natural language intent, decomposing it into structured tasks, and synthesizing results from multiple sources. What they cannot do---and what specialized agents can---is provide grounded, traceable, domain-precise reasoning for consequential decisions. |
The most effective architectures will combine both. A general model provides the flexible interface and coordination layer; specialized agents provide the domain expertise and reliability. The general model knows how to ask the right questions; the specialized agents know how to answer them with precision. |
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9.4 Future Trajectories |
Several trajectories are likely to shape the next phase of the specialization trend: |
From single agents to agent ecosystems: Organizations will deploy not one specialized agent but many, each serving a specific function, with orchestration frameworks managing their collaboration. The agent ecosystem will mirror the division of labor in human organizations. |
From reactive to proactive: Current agents largely respond to queries. Future agents will monitor data streams, detect anomalies or opportunities, and proactively surface insights. A manufacturing agent might flag an emerging equipment issue before it becomes critical; a financial agent might alert an analyst to a developing market pattern. |
From domain-specific to cross-domain orchestration: As agent ecosystems grow, the ability to coordinate across domains becomes critical. A patient's care might involve agents for oncology, cardiology, and pharmacology, all needing to share context and coordinate recommendations. |
From explainable to auditable: The demand for traceability will deepen. Regulators and institutions will require not just explanations but audit trails that can be independently verified. Agents will produce not just recommendations but the evidence and reasoning that support them. |
From assistive to autonomous (with oversight): The frontier is moving toward agents that can execute certain tasks autonomously within defined bounds, with human oversight focused on exceptions and high-stakes decisions. Chemotherapy order review, routine maintenance scheduling, and standard financial report generation are candidates for bounded autonomy. |
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9.5 The Manufacturing and Industrial Operations Context |
For readers of this book's Part V, the specialization trend has particular resonance. Manufacturing and industrial operations are domains where general models have struggled to deliver value because the work is grounded in physical reality---sensors, machines, materials, processes---that general models have never experienced. The specialized agent approach, grounded in industrial data and designed for traceable reasoning, offers a path to AI that actually works in the factory. |
The chapters that follow will explore specific applications in greater depth: predictive maintenance, quality control, supply chain optimization, and autonomous operations. The common thread will be the architecture described here: specialized agents, grounded in domain data, orchestrated for collaboration, with humans retaining judgment and accountability. |
The era of the general model as the answer to every problem is ending. The era of the specialized agent, precisely grounded and purpose-built, is beginning. |
cs@easiersoft.com