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INSIGHTS
6 min read
Published on 11/21/2024
Last updated on 11/21/2024
Why enterprises need to invest in agentic frameworks now
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The pace of GenAI and agentic technology evolution is relentless. Even for individuals and teams deeply immersed in the field, keeping up with advancements and breakthroughs in agent-based systems is challenging.
Hundreds of startups and established companies are actively investing in GenAI and agent technologies. During the first three quarters of 2024, venture capital funding for generative AI startups reached over $20 billion, reported by S&P Global Market Intelligence data. In June 2024, Cisco announced a $1 billion global investment fund targeted to developing AI solutions.
Investing in agentic frameworks is not just about staying current. It’s about securing a competitive edge and driving sustainable success. Enterprises that invest in these technologies today are positioning themselves for better outcomes by ensuring they can use the full capabilities of large language models (LLMs), which have become central to agentic applications.
For example, one area where AI agents are showing progress is in decision-making. In the next four years, Gartner predicts that at least 15% of people will make daily work decisions autonomously through agentic AI. As AI technology advances, organizations must consider the long-term implications of these systems. Building solutions that address how agentic systems will interact and integrate with each other will help future proof an organization’s operations.
Prioritizing speed and agility to see ROI
For any organization aiming to carve out a niche or lead in the agentic space, agility isn’t just an asset, it’s a necessity. Achieving success requires a tightly knit, small team of developers and product experts unified under a clear, collective vision. Organizations need to empower their teams to take the lead and minimize unnecessary bureaucratic hurdles and red tape. This streamlined approach can help achieve quick development, accelerated iterations, and pivots in response to new trends and discoveries.
Any deviation from this path risks producing a product that is sluggish to innovation, delayed to market, and ultimately rendered irrelevant in a space driven by extreme speed and innovation.
Agentic frameworks and technologies for a scalable foundation
Each new release of agentic frameworks pushes the industry closer to realizing the full potential of these systems. For this reason, enterprises need to act quickly if they want to see this functionality grow their organization. Leaders will need to determine where deploying agentic systems can make the most impact on their organization’s priorities.
OpenAI SDK, LangGraph, and LangChain exemplify the rapid development of new features and capabilities and demonstrate the speed and competitiveness of the field. With continuous improvements released, organizations need to act urgently if they want to keep a competitive advantage.
OpenAI SDKs (Software Development Kits)
Reflecting the swift pace of innovation, OpenAI released over 100 official SDKs. Releases closely tie to new models or significant features, such as Structured Outputs, which ensure that the model always generates responses that match a developer’s supplied JSON Schema.
Large language models (LLMs) are the central component in the architecture of agentic systems. Agentic applications rely on LLMs as their cornerstone. They possess powerful reasoning capabilities, can call tools, and generate structured output. Moreover, in an increasing multi-modal world, LLMs provide capabilities such as analyzing and generating images, video, and audio.
LangGraph
LangChain, the company behind LangGraph, has positioned this agentic framework as a leader in the space. Strong support from the developer community, an extensive feature set, and both local and remote GUIs bolster it. It also offers cloud integration along with built-in observability and tracing, making it a versatile choice for developers.
Additionally, LangGraph provides out-of-the-box support for agentic patterns, state management, a powerful composer, and extensive documentation, making it easier for developers to build complex agentic applications. LangGraph also uses LangChain APIs.
Further expanding its capabilities and appeal, LangGraph also supports distributed agentic applications.
With its frequent releases, the LangGraph project demonstrates an intense iteration cycle. Agentic frameworks need to support the increasing complexity of multi-agent systems. These systems are foundational of how GenAI applications interact with users and data, making improvements necessary for enterprises to stay competitive.
LangChain
LangChain is a flexible and extensible framework designed to simplify the development of language model driven applications. It enables seamless integration of LLMs with external tools, APIs, and databases, providing developers with the ability to chain various components together for advanced workflows.
Its modular approach allows for easy customization, from simple data pipelines to more sophisticated multi-step reasoning systems. With native support for prompt management, tool usage, and memory capabilities, LangChain is a go-to framework for developers building intelligent applications that require dynamic interactions with language models. Its strong community and frequent updates ensure it remains at the forefront of LLM-powered development.
LangChain’s release timeline demonstrates the progression of software development in the GenAI space. Between May and September 2024, there have been numerous releases, with versions ranging from 0.1.x to 0.3.0. This pattern indicates that LangChain is undergoing rapid development, marked by continuous improvements, bug fixes, and feature updates.
The value of investing in agentic systems and frameworks
By building their own agentic frameworks, enterprises can use the full potential of GenAI. Enterprises that invest in these technologies and include them in their AI roadmap will be better positioned for the pace of innovation in this field. Beyond technological exploration, fostering a culture where teams can focus fully their time on advancements in the space is necessary, in addition to speed. Teams need to be ring-fence to eliminate distractions to achieve swift development.
This blog is part of our series, Agentic Frameworks, a culmination of extensive research, experimentation, and hands-on coding with over 10 agentic frameworks and related technologies. In a subsequent blog post, we will dive deep into LangGraph, Llama Agents and OpenAI Swarm.
Outshift by Cisco is creating the foundation for the next generation of AI: an open, secure, and interoperable ecosystem for agentic AI. Stay up to date with our progress on creating agentic systems here.
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