Agents
Autonomous AI workers that handle complex conversations, follow defined procedures, feed shared context, and surface outcomes through custom interfaces.
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Define how agents think and act
Agents reason in real time to determine when to call tools, retrieve information, or escalate - transparently and within the boundaries you set.
Write your agent's persona, tone, and communication boundaries directly into the prompt, defining what it always says, never says, and how it handles sensitive topics.
Give agents step-by-step instructions for how to handle specific situations, like verifying identity before account access or confirming a waybill before sharing shipment data.
One agent brain across every channel
The same agent logic and knowledge works across voice, SMS, email, WhatsApp, web chat, Microsoft Teams, and Slack.
Agents maintain context across sessions, inject authenticated user data at session start, and process file uploads so customers never have to repeat themselves.
Escalations route instantly into channels of your choosing (Microsoft Teams, Slack, and more) when a conversation needs a human touch.
Human-like voices built for real world operations
Fine-tuned and proprietary text-to-speech (TTS), end-of-turn (EOT) detection, fillers, and voice activity detection (VAD) with keyword boosting and domain accuracy ensure that agents handle accents, jargon, and interruptions without breaking flow.
AI workers speak 30+ languages including English, Spanish, French, Arabic, and more with automatic failover and voice matching across providers so callers never notice a switch.
Dynamic denoising models and acoustic conditioning handle background noise, poor call quality, crosstalk, and messy environments, so agent performance doesn't degrade when conditions aren't perfect.
Every voice pipeline component is optimized for live telephony so conversations flow naturally without getting cut off mid-sentence or any awkward pauses.
Humans interact with agents through Interfaces
Purpose-built operational interfaces turn agent interactions into real-time visibility, action, and control for your teams.
Build interfaces for any use case including customer support, logistics operations, escalation management, and more, without external tooling.
Configure, monitor, and control your AI agents from a single environment — tailored to how each team actually works.
Enterprise Deployments
Launch your AI workforce today with the team, process, and expertise to deploy and scale with confidence
Cloud-native infrastructure built for enterprise scale
Kubernetes-based microservices across AWS, GCP, and Azure, with horizontal autoscaling, single-tenant deployment options, automatic AI model failover, and voice traffic segmented from application load for consistent latency at scale.
You choose how you deploy
HappyRobot builds your solution end to end while you co-own design and sign-off, or your engineers lead the build with full Forward Deployed Engineer (FDE) support on call and structured onsite training to ramp the team.
Established process to production
Deployments follow a consistent sequence: discovery and solution design, implementation, testing, and go-live - with both teams co-owning key decisions and sign-off at every stage.
Scale new markets on what’s already built
Every go-live includes a post-deployment care period with FDEs fully on call. The same team then scales every new market, with each region inheriting quality protections from every prior deployment.
Proactive intelligence
Describe what you want to build and the copilot drafts workflows, suggests northstars, wires up integrations, and generates evals starting from your intent, not a blank canvas.
An always-on agent watches your deployment, flags behavioral regressions, spots anomalies in audit pass rates, and identifies where workflows are losing efficiency - turning observability signals into specific, actionable recommendations.
Every audit result, human feedback signal, and production failure feeds back into the system. The monitoring agent translates that signal into concrete improvements.
The build copilot understands HappyRobot end to end — nodes, patterns, best practices, variable syntax. Recommendations are grounded in how the platform actually works.