Yazr AI
Autonomous multi-agent workflows with real-time voice, built over around 800 hours and rated 5.0.
Free 30-minute call. You leave with a written summary of the approach and a sensible first step. No deck, no pitch.
- Billed hours
- ~800
- Client rating on Upwork
- 5.0
Stack
- LangGraph
- LiveKit
- ElevenLabs
The starting point
The product needed agents that carry out multi-step work on their own: deciding which tool to call next, remembering where they were between steps, handing work to another agent when the task changed shape, and doing all of it while holding a real-time voice conversation with the user. A single prompt loop could not keep up with the branching, and a restart lost everything in flight.
What we built
01
Multi-agent execution graphs
LangGraph execution graphs split the work across agents with defined handoffs, so branches, retries and failure paths are explicit in the graph instead of improvised by the model on each run.
02
Structured tool calling
Agents call tools through typed schemas with narrow scopes instead of free-form text, so results are predictable, checkable and safe to act on.
03
Persistent state
Workflow state is saved as the agents work, so a deploy or a crash in the middle of a job is a pause, and the agents pick up where they left off.
04
Real-time voice
Voice pipelines on LiveKit and ElevenLabs sit in front of the graphs, so users talk to the agents in real time while the same tools and state run underneath.
What it came to
Autonomous agent workflows with real-time voice, delivered over around 800 billed hours and rated 5.0 by the client. The patterns from this build, typed tool contracts, persisted state and graphs with explicit retries, are what our AI agent and voice agent pages describe.
Planning an agent that has to run for more than one step, or talk to callers? Book a free architecture call. We will tell you on the call whether it needs a graph or a plain tool loop.
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