For an autonomous AI agent to be genuinely useful, it has to be able to access the business data required to perform its tasks. This means querying relational databases, reading from local code repositories, accessing SaaS applications over the cloud, and writing the results back to the appropriate systems. Previously, building connections between a language model runtime
The deployment of autonomous multi-agent networks requires a fundamental restructuring of enterprise organizational design. Previously, executives assessed software utility based on workforce productivity gains at an individual contributor level, asking how much faster could an employee perform a specific task with the aid of particular software, for example, how much faster could a worker write an email, analyze a spreadsheet, or write a script.
Autonomous agent networks render this metric
During the first wave of generative AI adoption, enterprise organizations focused primarily on generalist foundation models – which have broad language understanding and can engage in free-form writing and conversations. Yet, in regulated industries, large language models that lack precision, proper terminology, and logical rigidity often hallucinate, leading to erroneous output. Specialized agents working in a particular domain
