AI Agents

Put AI agents to work in the flow of execution.

Agantyx agents are built for work that has context, systems, approvals, and consequences. They can move through structured tasks, pull the information they need, coordinate with connected capabilities, and stop where human judgment should take over.

Execution surface

Bring agent execution out of isolated prompts and into the open.

AI agents in Agantyx work inside visible threads and runs. Teams can follow what the agent is doing, what it already completed, and where it is headed next, all without losing the working context around the task.

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Connected context

Create the most complete view of the work before the agent acts.

Agantyx agents can gather live context from connected systems, current documents, and process state before taking the next step. That gives the run a working picture of what is actually happening, not a manually reconstructed version of it.

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AI recaps

Summarize activity and save time with AI at your side.

Agents can turn long threads and active runs into readable recaps. Capture the decisions, surface the open issues, and bring the team back into the state of work quickly without forcing everyone to reread the entire history.

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Connected execution

Work across tools and specialist agents from the same operating layer.

Agantyx connects MCP services and A2A agents into one controlled run. That means the system can retrieve context, route work to the right capability, and bring the result back without losing control over state, traceability, or oversight.

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Expert agent in the thread

Bring an expert into the work surface while the run is moving.

Agantyx agents can answer, retrieve, route, and prepare the next step inside the same execution surface. That gives the team a usable expert response with context, supporting material, and a clear next action instead of a disconnected answer in a separate tool.

Frequently Asked Questions

What makes an Agantyx agent different from a normal chat assistant?

Agantyx agents are designed to run work inside a visible execution surface. They can follow plans, call tools, route through connected services, and pause for approvals instead of only generating one-off text responses.

Can agents use real company systems and files?

Yes. The real product supports connected services through MCP and delegated execution through A2A agents, so runs can retrieve context and route work through live capabilities rather than relying only on pasted inputs.

How do approvals work?

Agantyx supports manual gates and capability approvals so the run can stop when it reaches a step that needs human judgment. That keeps the agent useful without forcing the business to give up control.

Can teams inspect what the agent did?

Yes. Traceable steps and tool activity are part of the operating model. Teams can review what was called, what came back, and where the run is in its execution path.

Is this only for one department?

No. The same execution model can support engineering, operations, finance, service, and project delivery workflows as long as the task has structure, context, and a clear boundary for approvals.

When should a team start with AI agents?

Start with a workflow that already has repeated inputs, system handoffs, and a real need for traceability. That is where agent execution becomes measurable and operational rather than experimental.