Israeli startup xpander.ai has launched the Agent Graph System (AGS), which it says is a significant new method to constructing extra dependable and environment friendly multi-step AI brokers based mostly on underlying AI fashions akin to OpenAI’s GPT-4o collection.
The purpose is to redefine how AI brokers work together with APIs and different instruments, making superior automation duties extra accessible to organizations throughout industries.
From left: Ran Sheinberg, co-founder and chief product officer of xpander.ai and David (Dudu) Twizer, co-founder and CEO of xpander AI. Credit score: xpander.ai
Fixing the challenges of multi-step AI brokers
Operate calling, the spine of most AI agent workflows, allows fashions to work together with exterior programs to carry out duties akin to fetching real-time information or executing actions.
Nonetheless, these interactions typically falter when confronted with advanced API schemas or unpredictable responses, resulting in inefficiencies and errors.
xpander.ai’s Agent Graph System introduces a structured answer to those challenges through the use of a graph-based workflow that guides brokers by applicable API calls step-by-step.
As an alternative of presenting all out there instruments at each stage, AGS intelligently restricts choices to solely people who align with the present context of the duty, considerably decreasing out-of-sequence or conflicting perform calls.
Ran Sheinberg, co-founder and chief product officer at xpander.ai, defined in an interview with VentureBeat: “With AGS, we ensure the agent only uses the relevant tools at each step and follows the correct schema, enforcing precision and efficiency.”
Sheinberg beforehand labored at a number of different startups and as a principal options structure chief at Amazon Internet Companies (AWS), main large-scale compute initiatives with enterprise clients.
Democratizing AI agent growth
xpander.ai goals to make agentic AI growth accessible to a broader viewers. “We aimed to create an accessible platform that allows anyone to build AI agents, experiment with the technology, and start automating repetitive tasks to focus on what truly matters,” mentioned David Twizer, co-founder and CEO of xpander.ai, in the identical interview.
The corporate additionally provides AI-ready connectors that combine simply with NVIDIA NIM (Nvidia Inference Microservices) and different programs. These connectors enrich API instruments with detailed documentation, operational IDs, and schemas, decreasing the technical burden on builders whereas enhancing runtime accuracy.
“Once the setup is complete, you can connect it to any AI system that supports function calling,” Twizer mentioned. “It was crucial for us to design technology that meets customers where they are and offers flexibility to upgrade models over time.”
Twizer additionally beforehand labored at AWS as a principal options architect and chief of the go-to-market generative AI gross sales structure.
Key Advantages and Actual-World Impression
In benchmarking assessments, xpander.ai demonstrated that AGS, paired with its Agentic Interfaces, enabled AI brokers to realize a 98% success price in multi-step duties, in comparison with simply 24% for brokers utilizing conventional strategies.
These brokers accomplished workflows 38% sooner and with 31.5% fewer tokens, underscoring AGS’s capacity to cut back prices and enhance efficiency.
One real-world instance of AGS in motion concerned a benchmarking job the place an AI agent needed to analysis corporations throughout platforms like LinkedIn and Crunchbase, then arrange the leads to Notion. AGS streamlined the method, making certain instruments have been used within the appropriate sequence and schemas have been constantly adopted.
“We provide a complete AI agent that can create an interface to any system,” Twizer added. “The data interface, for the first time, is native to AI, addressing a major pain point the world is struggling with.”
AGS’s position in agentic AI
xpander.ai positions AGS as an important step within the evolution of agentic AI, enabling instruments like Nvidia NIM microservices to combine extra seamlessly with enterprise programs.
“AI agents will need to use APIs for synchronous use cases involving complex data structures, where traditional UIs just aren’t enough,” Sheinberg famous.
By means of AGS, xpander.ai transforms how AI brokers deal with error administration and context continuity. By embedding fallback choices instantly inside its graph buildings, AGS permits brokers to retry failed operations or pivot to different workflows with out human intervention, preserving job stability.
This stage of reliability ensures that AGS-equipped brokers usually are not simply reactive however adaptive, able to tackling even probably the most unpredictable workflows.
Constructing the way forward for AI workflows
xpander.ai’s introduction of AGS, coupled with its Agentic Interfaces, represents a big leap ahead for multi-step AI brokers.
By enabling structured, adaptive workflows and streamlining advanced API interactions, AGS units a brand new commonplace for reliability and effectivity in automation.
As the corporate continues to develop, its instruments promise to empower companies to harness the complete potential of AI-driven workflows.
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