Kylon vs Relevance AI
Relevance AI builds an AI workforce. Kylon puts agents in the room with the human one.
Kylon is a company harness where agents join your team, understand your business context, and execute real work across the tools you already use.
01 Which one is the right answer
Not every team should pick the same one.
Relevance AI is the right answer when
You are staffing a repeatable function, sales development or support triage, and want it drawn as a workforce with explicit handoffs and per step approval.
Kylon is the right answer when
The work is the team's daily work rather than one modelled process, everyone should be able to ask for it without being added as a builder, and the result belongs in the thread where the decision gets made.
02 Operating model
How each one works
Every statement about Relevance AI below cites Relevance AI's own documentation or pricing page. Checked 2026-09-09.
Where a run happens
A task is one run by an agent or a workforce. You watch it in the Run tab of the builder, or on a Tasks page that collects every task across all agents so you can triage escalations, errors and approvals in bulk.
Source: Tasks, Relevance AI docs
A run happens in the room where the team asked for it. The request, the work and the result are one thread that people and agents both read.
How you set one up
Agents are created by describing what you want with Invent, cloning from the marketplace, or building from scratch, then given tools and knowledge. A workforce is a team of agents connected on a drag and drop canvas, with handoffs the agent decides or you fix as a next step.
Source: Workforces, Relevance AI docs
You ask for the work in the room in plain language. It becomes a saved workflow when you want it to repeat, and the agents that carry it are workspace members with their own permissions.
Company data and its limits
Knowledge is a retrieval system fed by uploads and syncs from Drive, SharePoint and Notion. Published quotas are 100 MB of upload space, 50,000 rows per CSV and 10 MB of raw text as knowledge.
Source: System limits, Relevance AI docs
Connected services stay where they are, and the records the work runs on live in the workspace next to the thread, readable and writable by the agent without an upload step.
Approval before a write
Approval is configured per edge on the workforce canvas, with auto run, approval required and escalation modes, and reviewers act from the workforce task view. In Super GTM the default is to ask before writing to any integration.
Source: Approvals and escalations, Relevance AI docs
Outbound and sensitive actions wait for a person in the room where the work is being discussed, not in a separate review console. Email goes out as a draft a person sends.
Questions people ask
- Is Kylon a replacement for Relevance AI?
- Not if what you need is a modelled workforce for one repeatable function with an explicit canvas behind it. Kylon is the answer when the work is spread across a team's ordinary days and the agents have to work where that team already talks.
- Can everyone on the team use it?
- Yes. Anyone in the room can ask for the work directly. Nobody has to be set up as a builder first.
- Does anything get sent automatically?
- Email is drafted for a person to send, and sensitive actions wait for approval before they execute.
- How is Kylon different from Relevance AI?
- Relevance AI models one repeatable function as a workforce with explicit handoffs. Kylon covers a team's ordinary daily work: the request starts as a message, agents carry different parts of it, and the result returns to the thread where the decision gets made.
- Can we keep Relevance AI and use Kylon?
- Yes. A modelled function and a team's daily work are different jobs, and Kylon reads the tools that work runs on rather than replacing them.
- What does Kylon cost?
- Kylon is a paid workspace. Current plans are on the pricing page.
Keep reading
A workforce you have to draw is still a workforce someone has to run.
Put agents in the room where the work is already discussed, give them the records and tools the job needs, and review what they hand back.