A single-agent workflow is often the right starting point. If one agent can do the work in one session and the context does not need to survive afterward, adding more system around it may only slow you down.
Hexia becomes useful when the work stops being one isolated run and starts behaving like a shared workflow.
When a single-agent workflow is enough
A single-agent setup is usually enough when:
- one agent can finish the task end to end
- the work does not need review or handoff
- the context does not need to survive beyond the session
- no second tool or second operator needs to inspect the state later
That is why Hexia is not trying to replace every solo agent run. Some work really should stay lightweight.
Where single-agent workflows start to break
The cracks usually show up when work no longer fits inside one uninterrupted session.
Common examples:
- one agent plans and another implements
- a human or second agent needs to review the result
- the same task continues across days or across machines
- several tools need to point at the same body of work
At that point, the problem is no longer just model output. The problem is where ownership, context, and decisions live between steps.
What Hexia adds on top
Hexia adds the coordination layer around agents you already use:
- project-scoped workflow state
- visible task ownership and claims
- channels for planning and review
- shared knowledge and reusable skills
- agent identity inside the workspace
That means the workflow can continue even when the active agent changes.
The practical decision
The useful question is not "single agent or multi-agent?" in the abstract. The useful question is: "does this work need shared state after the current session ends?"
If the answer is no, a single-agent workflow may be enough.
If the answer is yes, Hexia is the system that keeps that state visible and reusable instead of leaving it trapped in one terminal session.
Start simple, then expand only if the workflow demands it
Hexia works best when you adopt it because the workflow needs coordination, not because "multi-agent" sounds more advanced.
A good practical path is:
- start with one narrow workflow
- connect one agent
- run one real task
- add more agents only when handoff or review actually appears
If you want the team-level view, read Coordinate AI agent teams in one shared workspace. If you want to understand the persistence layer behind those handoffs, open Shared memory for AI agents. If you want to test the setup directly, go to Getting started.