Governed for Agents

One second brain. Every agent you run. One governed source.

ContextNest is the knowledge layer every agent you deploy reads from — versioned, approved, the same context every time. PromptOwl turns it into agents you actually run, and controls which ones can act on it.

Wiring your own agents? Developer quickstart and MCP setup →

Try it yourself.

Set up your context and your first agent below — see how easy it is.

app.promptowl.ai/magic-context

Try the real onboarding flow right here. It loads the live PromptOwl app.

Same Question, Different Source

One agent read a stale copy. The other read the vault.

Point two agents at the same question and you get two different answers — one from whatever copy they happened to have, one from the version everyone agreed is current. Only one of those is worth shipping.

S
Support Agent, reading a stale copy
what's our refund window on annual contracts?

“Annual contracts can be refunded within 60 days, and we waive the setup fee for nonprofits.”

↩Refund window changed to 30 days in March
↩Nonprofit setup-fee waiver ended a year ago
S
Support Agent, reading the governed Prime doc
what's our refund window on annual contracts?

“Annual contracts can be refunded within 30 days of signing. There's no setup-fee waiver running right now.”

↩Current policy, read straight from Prime

Governed, Not Guessed

A governed vault doesn't just organize your knowledge. It makes every agent that reads it more accurate.

97%

Answer pass rate vs. 90–93% for keyword search (BM25), in a stale-version test. Preprint, small corpus.

About ⅓

Of BM25's input tokens in the same stale-version test. Preprint, small corpus.

Same context

For the same question, every time. Vector RAG varied on 80% of repeated queries in our preprint.

What you'd try instead, and where it breaks.

Each of these works for one agent. They break the moment you add a second agent or switch models.

A shared drive, wiki or vector database

Access isn't authority. These find things. They don't tell an agent what's actually true right now, so drafts and old versions answer too.

Full comparison →

Letting each agent remember on its own

One agent gets memory inside one tool. Add a second agent or switch models and it's back to zero.

Full comparison →

Wiring knowledge into each agent framework

Frameworks govern what an agent may do. Which knowledge it may treat as true is left to every app, separately.

Full comparison →

You don't need a platform team. You need a vault your agents can reach.

Run this hosted on PromptOwl — always-on infrastructure, nothing to patch or provision before your first agent reads from something real. Any agent that speaks MCP can connect to it. Need the vault on your own infrastructure instead? That's available too.

Set up in an afternoon, not a quarter

Go hosted and skip the infrastructure work entirely. Point ContextNest at what your team already knows, and the next agent you build reads from it before the day is out.

Any agent that speaks MCP can connect

Claude, ChatGPT, Cursor, an internal tool you built last quarter — if it's MCP-compatible, it reads from the same governed vault. No custom integration per agent.

Self-hosted when you need it

Regulated data or a security team that requires zero external calls? ContextNest runs entirely on your own infrastructure too — same governance, your servers.

How It Works

From Scattered Knowledge to Agents You Can Trust.

onboarding-runbook.md
v3 · Approved
#process#onboarding#support

Organize

ContextNest turns what your team already knows — docs, decisions, runbooks — into a structured, version-controlled vault: typed, graph-linked, human-readable Markdown. Every agent starts from the same source, not five different ones.

Connected via MCP
ClaudeConnected
ChatGPTConnected
CursorConnected

Connect

Any MCP-compatible agent reads directly from your vault — the ones you've already built, and the ones you build next. Same governed context, every agent, every session.

Live Agents2 Active
S
Support Agent
O
Onboarding Agent

Deploy

PromptOwl turns the vault into agents you can run — support, internal tools, whatever the work calls for — and governs which ones can act on it, with an audit trail for every one.

Organize and Connect are ContextNest, and they are free. Deploy is PromptOwl, whenever you're ready to build.

Most AI tools give you access to agents. PromptOwl gives you control over what they know and do.

The teams pulling ahead didn't just deploy more agents. They organized what those agents know into something governed — once — so every agent they ship since starts from the same source instead of its own private guess. Here's what that looks like below.

One Governed Source, Every Agent

Every agent reads the same versioned, approved knowledge.

ContextNest doesn't just store what your team writes down. It governs it. Every document is versioned, every edit is logged, and you decide what's Prime, the version every agent is required to treat as true. When a policy changes, every agent connected to the vault gets the update. None of them keep working from what used to be true.

Refund Policy
Prime
v4 — approvedCurrent
v3Superseded
v2Superseded

Model Access

Configure once. Switch freely — even mid-deployment.

Your context is governed once. Your agents are built once. Then you choose the model underneath — ChatGPT, Claude, Gemini, or any other major model — and every agent keeps working. No rebuilding a support bot from scratch because a sharper model shipped. Swap the engine. The agent, and the knowledge it runs on, stays intact.

Model Engine
Active
G
GPT-5.0
C
Claude 4.7 Opus
G
Gemini 3.1 Pro

Deploy & Govern

Every agent, one governed source.

PromptOwl is the easiest way to build tools on top of that vault, run them on a bank of included credits, and keep them on always-on infrastructure you never have to patch or upgrade yourself. Every action is logged, so when someone asks what an agent did, and what it knew when it did it, the audit trail has the answer. It's knowledge management and agent management in one place, not two systems stitched together — so the fifth agent you add doesn't become a fifth ungoverned island of knowledge.

Governed Agents3 Active
S
Support Agent — reads Prime docs
O
Onboarding Agent — reads Prime docs
M
Marketing Draft Agent — pending review

Trust the Source

Agents you can trust and control.

app.promptowl.ai/prompt/monitor
HistoryAnnotations
Testing: v9 · Production
TopicVersionUserStart TimeSource
SOC 2 and HIPAA controls on every planHIPAA BAA on EnterpriseGDPR-ready
  • Every agent's actions are logged, and every answer is sourced.Ask what an agent did, and what it knew when it did it — the audit trail has both, not just one.
  • Your data stays yours. What goes in never trains a public model. See the trust center for the full evidence.

Start Today

One Product. Free to Start, Priced to Scale.

PromptOwl includes ContextNest free — install it, organize what your team knows, connect every agent you already run. Upgrade when you need the audit trail of what every agent did and knew, agents you build by describing what you want and version like code, and role-based access for your whole team.

Start Here

PromptOwl — Free

Free for up to 10 users · No card required

Install the CLI or desktop app, organize your team's knowledge into a governed vault, and connect any MCP-compatible agent you already run. Private, self-hosted, yours.

Get PromptOwl free
When You're Ready

PromptOwl — Hosted

$10 per user a month, billed annually

The easiest way to build tools on your vault, run them on 5,000 credits per user each month, and keep a full audit trail of what every agent did and knew. Every major model included.

See PromptOwl plans

Running agents across a whole organization, with compliance on the line? That's a different conversation — audit trails, access controls, and a security team that needs answers before you deploy.

AICPA SOC badge
HIPAA BAA
on Enterprise
GDPR-ready
See the Enterprise path →
FAQ

Common Questions.

We already have a few agents running in different tools. Why do we need this?

Each of those agents is probably working from its own private slice of your knowledge — one bot's upload, another tool's static prompt, a third one's guess. ContextNest gives all of them one governed source instead. You don't rebuild the agents. You point them at something real.

Do we need to rebuild our existing agents to use this?

No. Any MCP-compatible agent can read from your ContextNest vault without a rewrite — you're changing what it reads from, not how it was built. If an agent isn't MCP-compatible yet, that's usually a connection change, not a rebuild.

Why not just point our agents at a shared drive, a wiki, or a plain vector database?

Because access isn't the same as authority. A shared drive or a vector database finds things — it doesn't tell an agent what's actually true right now. ContextNest separates raw text from approved truth: agents only read what a human steward has verified and versioned, not whatever's floating around in a folder somewhere. Finding is different from trusting.

Couldn't one agent just remember things on its own, the way some AI tools do now?

That gives one agent persistent memory inside one tool. The moment you add a second agent, or switch models, it's back to zero. ContextNest is the same governed vault behind every agent and every model you use — memory that travels with your team, not memory locked inside one chat window.

What's the difference between ContextNest and PromptOwl?

ContextNest is the vault — free to start, with an open-source (AGPL-3.0) command-line tool, engine and MCP server, and the same knowledge layer every agent reads from. PromptOwl is the governed layer on top of it: it turns that vault into agents you can run, decides which ones can act, and keeps the audit trail. Two jobs, one governed source.

Can ContextNest build agents on its own?

ContextNest organizes and governs the knowledge — it doesn't run agents itself. PromptOwl is what turns a governed vault into an agent you can run and control. Whatever you've already built in ContextNest carries straight over — nothing to rebuild from scratch.

Does this replace our RAG setup?

Not necessarily — they solve different problems. ContextNest handles knowledge that changes and needs approval: policies, decisions, runbooks. RAG is still the right tool for large, static document sets. PromptOwl governs both as connected layers, so your agents can draw on either without you managing two disconnected systems.

Does our knowledge have to leave our infrastructure?

No. Most teams run this hosted on PromptOwl, and it still doesn't train on your data — PromptOwl runs its own model layer, so your knowledge and prompts never touch a public training pipeline. If your team needs the vault to stay entirely on your own infrastructure, ContextNest also runs fully self-hosted, and any MCP-compatible agent can point at it right where it sits.

Is PromptOwl compliant enough for our security team?

Every plan runs on the same SOC 2 and HIPAA controls. SOC 2 certification and reporting and a HIPAA BAA come with Enterprise. Every agent action is logged, and data processing agreements are available on request.

See the trust center for the full evidence

Every Agent You Run, Accountable. Starts Today.

Free to install. No card required. Organize what your team knows, connect the agents you already run, and upgrade whenever you're ready to build more.

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