SaaS is dead, long live Agentic API: Part 1

SaaS end of life to be declared soon

Current Software as a Service business model exploits a concept of technology accessibility for more than two decades. After dotcom bust at the end of XX century there was a necessity to materialize an importance of Internet as a source of technological breakthrough.

That was an obvious choice and evolutional step forward in providing a piece of software accessible via Internet. The paradigm solved the issue of accessing various technologies via instant and useful subscription models.

That was a true revolutional approach that gave possibility to access a software beyond local Windows, Mac, Linux machines. End users or business customers were allowed to benefit from the latest trends of software frameworks and stacks materialized via SaaS subscriptions. SaaS gave users an access to technologies regardless effectiveness of their usage and applicability of those to real business cases.

SaaS being a paradigm for a majority of enterprise business cases, ruled the software world as the King of Internet era. Now it seems those times come to the end of life.

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Agentic API as a Service

Once the software world is undergoing an evolutional transformation from Technology Accessibility to Knowledge Accessibility, SaaS paradigm is inevitably being transformed into AI paradigm. AI Agents serve as Knowledge APIs focused on specific business cases.

Hence the core paradigm shift is expressed in not sharing Technological Access( SaaS ) but in sharing access to Knowledge and Effectiveness of business processes. Instead of accessing a technology you might( or might not ) use effectively, customers access effectiveness and robustness of their core businesses.

Agentic AI uses several steps in problem-solving:

Perceive: AI agents gather and process data from various sources, such as sensors, databases and digital interfaces.

Reason: A large language model acts as the orchestrator, or reasoning engine, that understands tasks, generates solutions and coordinates specialized models for specific functions. Retrieval-augmented generation (RAG) is used to access proprietary data sources and deliver relevant outputs.

Act: By integrating with external tools and software via application programming interfaces, agentic AI can quickly execute tasks based on the plans it has formulated. Guardrails can be built into AI agents to help ensure they execute tasks correctly. For example, a Customer Service Representative( CSR ) AI agent may be able to process technical support tickets to a certain technical level of expertise based on whether a user is proficient in a given technology.

Learn: Agentic AI continuously improves through a feedback loop, or Knowledge Base where the data generated from its interactions is fed into the system to enhance models. This ability to adapt and become more effective over time offers businesses a powerful tool for driving better decision-making and operational efficiency.

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Agentic AI integration with Enterprise Data

Across industries and job functions, generative AI is transforming organizations by turning vast amounts of data into actionable knowledge, helping employees work more efficiently.

AI agents build on this potential by accessing diverse data through accelerated AI query engines, which process, store and retrieve information to enhance generative AI models. A key technique for achieving this is RAG, which allows AI to tap into a broader range of data sources.

Over time, AI agents learn and improve by creating a data Knowledge Base, where data generated through interactions is fed back into the system, refining models and increasing their effectiveness.

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Agentic API in action

Agentic AI being defined as a business process effectiveness and result oriented software, is consumed via APIs focused around specific business domains.

Enterprise Security: AI agents pro-actively monitor corporate endpoints for vulnerabilities, risks associated with malware and harmful attacks. Protection plan and active sanity checks are advised to a company security team and product stake holders.

Customer Service: AI agents improve customer support by enhancing self-service capabilities and automating repetitive tasks. Over half of service professionals report significant improvements in customer interactions, reducing response times and boosting satisfaction.

Content Creation: Agentic AI can help quickly create high-quality content. Generative AI agents can save marketers a big chunk of work, allowing them to focus on strategy and innovation. By streamlining content creation, businesses can stay competitive
while improving customer engagement.

Software Engineering: AI agents are boosting developer productivity by automating repetitive coding tasks freeing developers to focus on more complex challenges and drive innovation.

Healthcare: For doctors analyzing vast amounts of medical and patient data, AI agents can distill critical information to help them make better-informed care decisions. Automating administrative tasks and capturing clinical notes in patient appointments reduces the burden of time-consuming tasks, allowing doctors to focus on developing a doctor-patient connection. AI agents can also provide 24/7 support, offering information on prescribed medication usage, appointment scheduling and reminders, and more to help patients adhere to treatment plans.

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SpartanShield ModelRouter Platform and its Agentic API

Spartans provides not only for automatic provisioning of business specific LLMs to local customer Endpoints but also exposes its Agentic AI business cases via APIs.

Customers can be sure that their business Knowledge Bases are private, secured and as effective as possible.

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