Floxar in an AI stack
Putting AI agents to work on real tasks takes several layers of technology, each with its own job. These articles explain the job Floxar does among them: holding how your organization expects work to be done and recording how it was done, alongside your models, agents, documentation and business systems, with practical patterns for designing AI workloads around it.
Where Floxar fits in an AI stack
An organization that puts AI agents to work on real tasks ends up with several layers of technology, each answering a different question. Floxar is one of those layers: the one that holds how your organization expects the work to be done, and records what actually happened when it was done. It is not the AI that does the work, and it does not replace the systems the work touches.
Floxar, documentation and knowledge
Most organizations already have a great deal written down: procedures in documents, answers in a wiki, policies in shared folders, and a knowledge base that people and AI tools search. Floxar does not ask you to throw any of that away. It changes one thing: the part of your documentation that describes how work is done becomes something people and agents run, instead of something they read.
Choosing AI providers and models
With Floxar, how your work is done does not belong to any AI provider. Your processes live in Floxar as flows, and the record of how they ran lives in Floxar as trails. The AI that runs a step is your choice, and you can change it whenever you like: a provider and model today, a different one tomorrow, or a different model for different kinds of work.
Designing AI workloads with Floxar
Putting an AI agent to work on a real process is mostly a design question: what the agent should do, what it should leave to code, and when it should hand over to a person. These patterns describe how to answer it with Floxar. They apply whichever agent you use: one you build, an AI client your people connect, or, once it is available, the Operator.
Version control and promoting content
Flows and bits are the definition of how your organization works, and many teams want to treat them the way they treat code: kept under version control, reviewed before they change, tested away from live work, and then promoted to production. This guide describes how to do that with Floxar today.
Use case: incident response runbooks
Runbooks go stale, and an AI agent let loose on a production incident without one is a risk nobody wants to take. With Floxar, the runbook becomes a flow that your engineers and your agents both run: the agent does the fast, repeatable diagnosis, and the on-call engineer takes over before anything risky happens.
Use case: employee offboarding
When someone leaves, several teams each have a part to play, and the gaps between them are where things go wrong: an account left open, a laptop never returned, a policy applied to one leaver and not the next. With Floxar, offboarding is one flow that moves across teams, with an AI agent taking the repeatable work and every team's part recorded on the same trail.
Use case: invoice exception handling
In accounts payable, procedures have to be followed exactly, and the invoices that do not match their purchase order take most of the time. With Floxar, the exception procedure is a flow: an AI agent does the comparison and applies the policy, a rules service decides what is within tolerance, and a specialist decides the rest, with every item's handling recorded.
Use case: customer billing disputes
Support teams answer billing disputes from knowledge bases that are hard to search, new people lack confidence, and leaders cannot see how cases are actually handled. With Floxar, the dispute procedure is a flow that people and AI agents both run: the agent gathers the facts and applies the refund policy, and the team lead decides the exceptions with the research already done.