Enterprise Agentic AI
Governed by Design
Collaborative Agentics.
For humans.
Voyageur-AI is the fully configurable, no-code enterprise platform that enables teams to deploy agentic AI with built-in governance, shared context, and a human accountability layer at every step.
Configurable by design. No code required.
Every dimension of Voyageur's agentic infrastructure is configuration-driven — from the models powering each agent, to the knowledge sources they draw from, to the tools they're permitted to use and the autonomy they're granted to use them.
BYO-LLM · Per-Agent Model Configuration
Choose the right model for each agent role. Assign a frontier model to your compliance agent, a cost-efficient model to your document pipeline, a self-hosted model where data can't leave your network — all from configuration, not code. Swap providers without rebuilding workflows.
Model AgnosticBYO-RAG · Configurable Knowledge Architecture
Bring your own knowledge stores. Voyageur supports ingestible document corpora, configurable external RAG sources, and enterprise-grade hybrid search — combining vector, lexical, and semantic retrieval across your organizational knowledge.
BYO-RAGOpenAPI Capabilities · Configurable Agent Tools
No custom integration code. No rebuilding agent logic. Connect any OpenAPI-described service and Voyageur configures what your agents can do — and can’t. Change, restrict, or extend agent capabilities through configuration alone, without touching core behavior.
OPENAPI-DRIVENConfigurable Autonomy Levels
Every capability an agent holds carries a configurable autonomy setting — from fully supervised (human approval required) to fully autonomous (execute and log). Set autonomy at the capability level, not the agent level, for surgical control.
Granular GovernanceAgentic Personas & Playbook Templates
Define AI personas with scoped roles, responsibilities, and knowledge access. Build reusable agentic playbooks — multi-phase workflow templates that encode your organization's processes and can be deployed across teams instantly.
No-Code DeploymentMulti-User Collaboration with Shared Context
In Voyageur, context boundaries aren’t just for humans — they govern what your agents know and can act on. Topics, artifacts, and conversations are scoped by role and delegation. Agents operate within those boundaries automatically, so collaboration scales without leaking context across teams or trust zones.
TEAM INTELLIGENCE“Voyageur is not primarily about making agents more capable.
It is about making agentic work governable, inspectable, and sustainable for humans.”
This is the principle that makes enterprise-grade agentic AI work in practice: the future is not just more capable agents, but agents that humans can work with.
Voyageur at a Glance
Seven pillars. Governance built in at every layer.
Persona & Role Design
View screenshots (7) →Every agent in Voyageur has a defined identity — a persona with scoped roles, responsibilities, knowledge access, and reasoning modes that shape how it thinks and responds. Personas are not prompts. They are versioned, publishable configurations that govern agent behavior before a single conversation begins. Autonomy levels are set per capability, from fully supervised to fully autonomous.
Voyageur operates on two distinct planes — the organizational and the personal — with a clean, enforced boundary between them. Within the organizational plane, every persona, capability, and knowledge access point is defined, governed, and auditable by the enterprise. Nothing from outside that boundary operates inside it without explicit authorization.
At the same time, individuals bring their own trusted agent relationships with them — a personal identity that exists independently of any organization they work within. That personal context never crosses into the organizational plane uninvited. The individual is recognized. Their outside world is not.
The boundary between planes is managed through Addressable Intent — any named persona can be addressed at any time, and Voyageur scopes the conversation to the appropriate plane automatically. No menus. No toggles. No friction.
Here’s what that looks like in practice:
Daniel: Alfred, on the notification service spec — let’s add a user preference for notification frequency. Daily digest or real-time. That should keep the noise down for users who don’t need instant updates.
Alfred: Good call. I’ve added a notification_frequency preference with realtime and daily_digest options to the spec. Defaulting to daily_digest — users can always opt up.
Daniel: Good. Ok — Judy, I just remembered I need to look at flights to Greece. What are the options from JFK to Athens the second week of July? Paula and I are thinking maybe the 12th.
Judy: Switching to your personal workspace — I’ll keep this between us. Give me a moment... Ok, you have three options for July 12th from JFK to Athens. Aegean Airlines direct — departs 11:10am, arrives Athens 6:05am the 13th, about ten hours. British Airways connecting through London. Red-eye on Delta that saves about $200 but lands you in Athens exhausted.
Daniel: Send those to Paula and see if she has a preference.
Judy: Done. I’ll let you know what she says.
Daniel: Alfred — let’s get back to it. Go ahead and build the epic.
Alfred: Welcome back to the notification service. Drafting the epic now — let me know if this looks good and I’ll send it over to Dawn.
After taking care of the personal item with Judy, Daniel’s work thread resumes exactly where it left off. On the organizational plane, Alfred has the full context — the conversation history, the accumulated artifacts, the decisions already made about the notification service. On the personal plane, Judy has only the conversational context related to a trip to Greece. Daniel’s use of the persona name is the switch.
Task Delegation:
When the epic is drafted and Daniel says, “Build the epic”, Voyageur recognizes that Daniel doesn’t have the authority to create epics in Jira directly. Rather than stopping him in his tracks, it packages the work and routes it to Dawn — the project manager and default owner of the Jira integration — for review and creation. Alfred surfaces the handoff naturally:
“I’ve packaged up the notification service epic and delivered it to Dawn for review and creation. I’ll let you know when it’s been added.”
The work never stopped. The governance never showed. Daniel didn’t hit a wall — he was guided around one, and the right person was brought in at exactly the right moment. The vacation plans are in Paula’s hands. The epic is on Dawn’s desk. Neither plane knew the other existed.
The right agent. The right context. The right authority. Every time.
01 — Persona & Role Design
Persona & Role Design
Every agent in Voyageur has a defined identity — a persona with scoped roles, responsibilities, knowledge access, and reasoning modes that shape how it thinks and responds. Personas are not prompts. They are versioned, publishable configurations that govern agent behavior before a single conversation begins. Autonomy levels are set per capability, from fully supervised to fully autonomous.
Voyageur operates on two distinct planes — the organizational and the personal — with a clean, enforced boundary between them. Within the organizational plane, every persona, capability, and knowledge access point is defined, governed, and auditable by the enterprise. Nothing from outside that boundary operates inside it without explicit authorization.
At the same time, individuals bring their own trusted agent relationships with them — a personal identity that exists independently of any organization they work within. That personal context never crosses into the organizational plane uninvited. The individual is recognized. Their outside world is not.
The boundary between planes is managed through Addressable Intent — any named persona can be addressed at any time, and Voyageur scopes the conversation to the appropriate plane automatically. No menus. No toggles. No friction.
Here’s what that looks like in practice:
Daniel: Alfred, on the notification service spec — let’s add a user preference for notification frequency. Daily digest or real-time. That should keep the noise down for users who don’t need instant updates.
Alfred: Good call. I’ve added a notification_frequency preference with realtime and daily_digest options to the spec. Defaulting to daily_digest — users can always opt up.
Daniel: Good. Ok — Judy, I just remembered I need to look at flights to Greece. What are the options from JFK to Athens the second week of July? Paula and I are thinking maybe the 12th.
Judy: Switching to your personal workspace — I’ll keep this between us. Give me a moment... Ok, you have three options for July 12th from JFK to Athens. Aegean Airlines direct — departs 11:10am, arrives Athens 6:05am the 13th, about ten hours. British Airways connecting through London. Red-eye on Delta that saves about $200 but lands you in Athens exhausted.
Daniel: Send those to Paula and see if she has a preference.
Judy: Done. I’ll let you know what she says.
Daniel: Alfred — let’s get back to it. Go ahead and build the epic.
Alfred: Welcome back to the notification service. Drafting the epic now — let me know if this looks good and I’ll send it over to Dawn.
After taking care of the personal item with Judy, Daniel’s work thread resumes exactly where it left off. On the organizational plane, Alfred has the full context — the conversation history, the accumulated artifacts, the decisions already made about the notification service. On the personal plane, Judy has only the conversational context related to a trip to Greece. Daniel’s use of the persona name is the switch.
Task Delegation:
When the epic is drafted and Daniel says, “Build the epic”, Voyageur recognizes that Daniel doesn’t have the authority to create epics in Jira directly. Rather than stopping him in his tracks, it packages the work and routes it to Dawn — the project manager and default owner of the Jira integration — for review and creation. Alfred surfaces the handoff naturally:
“I’ve packaged up the notification service epic and delivered it to Dawn for review and creation. I’ll let you know when it’s been added.”
The work never stopped. The governance never showed. Daniel didn’t hit a wall — he was guided around one, and the right person was brought in at exactly the right moment. The vacation plans are in Paula’s hands. The epic is on Dawn’s desk. Neither plane knew the other existed.
The right agent. The right context. The right authority. Every time.
From pilot to production — the governance gap
78% of enterprises have at least one AI agent pilot running. Only 14% have successfully scaled to production1. The gap isn’t capability.
The tooling has matured. The models are ready. What’s missing is the accountability layer that makes agentic AI trustworthy enough to operate at organizational scale.
Organizations that implemented AI governance pushed 12 times more projects into production than those that didn’t2. Governance isn’t a constraint on AI velocity. It’s what enables it.
Voyageur is built for organizations at that gap — with working pilots, real intent, and the need for infrastructure that can carry agentic AI from promising to production.
1 Digital Applied, “AI Agent Scaling Gap March 2026: Pilot to Production”, March 2026.
2 Databricks, “Enterprise AI Agent Trends: Top Use Cases, Governance + Evaluations and More”, January 2026.
Configurable Autonomy
From human-approved to fully autonomous — set at the capability level, not the agent level. The right autonomy isn’t the most autonomy. It’s what your organization has authorized.
Organizational Memory
Knowledge that compounds rather than resets. Every conversation, every artifact, every decision — captured, versioned, and available to the next workflow.
We’re looking for people who understand the problem and want to help build the solution.
Voyageur is nearing MVP and we’re actively seeking collaborators — not just customers — who want to shape the platform from the inside. If you’re an enterprise practitioner, AI governance researcher, or technical leader who’s been wrestling with the agentic AI implementation gap, we’d like to hear from you.
We’re also open to conversations with potential design partners, integration collaborators, and investors who see what we see in this space.
Design Partners
Organizations with working AI POCs that are stuck at the governance wall. Help shape the product that removes it.
Technical Collaborators
Architects, AI engineers, and integration specialists who want to build on — or alongside — Voyageur's governance layer.
Investors & Strategic Partners
Early-stage investors and strategic partners who understand the enterprise AI governance market and where it's going.
Solving the Problem of Cognitive Load in the Age of Agentic AI
Michael Truell, CEO of Cursor, writing in April 2026:
"Cursor is no longer primarily about writing code. It is about helping developers build the factory that creates their software. This factory is made up of fleets of agents… A year from now, we think the vast majority of development work will be done by these kinds of agents."
— Michael Truell, CEO, Cursor · April 2026
Truell is right about where we are heading. What his vision leaves unaddressed is the “how.” How will a human stay in command of a growing number of agents operating faster than any person can meaningfully review? How will a human withstand the cognitive burden of all that output? Unless the agentic workplace is designed from the human perspective rather than the capability perspective, agents will not eliminate cognitive load. They will accelerate it.
"The human role shifts from guiding each line of code to defining the problem and setting review criteria… Agents write almost 100% of their code. They spend their time breaking down problems, reviewing artifacts, and giving feedback."
— Michael Truell, CEO, Cursor · April 2026
We use Cursor every day to build Voyageur. We are not observing this shift from a distance — we are living it, finding the path forward in real time. That firsthand experience is precisely what led us to build the foundation that the agentic era needs but has not yet recognized.
For organizations to benefit broadly from agentics, agentic infrastructure must be widely accessible. Must be flexible. Must naturally accommodate the variability humans find in their daily work. It must welcome changes to workflows and processes, and make it both intuitive and frictionless to alter agentic behavior when working conditions change. These are the problems we are focused on solving at Voyageur because agentic workflows that require code changes and deployments cannot scale. Organizations survive through adaptation. Their agentics must be adaptable, or the very technology that promises transformation will be the technology that slows them down.
AI is accelerating output faster than organizations can understand, verify, or govern.
Decisions are made. Documents are drafted. Code is written. Systems change. But the reasoning behind them — the intent, the context, the constraints — is often fragmented across chats, tools, and individuals, making the full picture difficult to reconstruct. This was always the challenge. Agentic AI has only compounded it.
The stakes are measurable: nearly eight in ten companies have deployed generative AI — yet just as many report no material impact on earnings.1 Not because the technology isn’t capable. Because capability without accountability doesn’t produce durable value. It produces faster output with less visibility into what it means, where it came from, and who is responsible for it.
More autonomy alone is not the answer. Systems that generate faster than people can understand, verify, and carry forward don’t create advantage. They create fragility.
The organizations that will thrive in the agentic era are not the ones that deploy the most agents. They are the ones that preserve clarity, traceability, and human judgment at every consequential decision point — regardless of how much of the work an agent performed.
Voyageur doesn’t bring you more agentic problems.
It gives you the surface you need to solve the ones you already have.
1 “Seizing the Agentic AI Advantage,” QuantumBlack, AI by McKinsey, June 2025.
→ How It WorksThe Voyageur Philosophy of Collaborative Agentics
AI should advance human intent through governed, explainable, jointly-produced actions — in true symbiotic alignment. Not AI replacing humans. Not humans managing AI step-by-step. Something better: a genuine partnership where each does what it does best.
Humans bring what no machine can replicate — inspiration, judgment, contextual understanding, and accountability. Agents bring structure, momentum, and executional clarity. When that balance is honored, the work accelerates without the human disappearing from it.
Voyageur agents operate within explicitly defined boundaries — boundaries that humans set, can modify, and can audit at any time. Every capability carries a configurable autonomy level, from fully supervised to fully autonomous. Every action is traceable to the authorization behind it. That’s not a limitation on what agents can do. It’s what makes it possible to trust them with more.
Read the full Voyageur Philosophy →We’re looking for early partners.
Voyageur is nearing MVP. We’re seeking a small number of organizations who have the problem we solve — and want to help shape the platform that solves it.
We’re interested in serious conversations about where enterprise agentic AI is working, where it’s stalling, and what governed collaboration actually needs to look like in practice. If that’s a conversation you’d find valuable, let’s talk.