Agent Manager

A team of AI specialists,
on your firm’s real work.

Not a chatbot that answers questions — a coordinated team of AI specialists that plans a job, does it against your firm’s own documents and data, checks its own work, and hands you a finished result for sign-off.

And it does all of this without ever taking your data out of your hands.

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The problem with AI at work today

Every firm has tried the chatbots.

They’re impressive in a demo and frustrating in practice, for four reasons that matter to anyone running a real business.

They don't know your firm.

A generic model has never seen your precedents, your templates, your house positions, or how you do things. It produces plausible, generic work that a senior person then has to rewrite.

They don't get better.

Correct the same mistake ten times and you'll correct it an eleventh. Nothing the AI does today makes it smarter tomorrow.

They can't be supervised.

No record of what the AI looked at, no control over what it's allowed to touch, and no human approval step before its output goes out the door.

They want your data.

Most "AI over your documents" products make you upload everything into their system — and once it's there, keeping it current, secure, and compliant becomes your problem.

Real knowledge work is a team activity with roles, memory, judgment, and oversight. It has never fit inside a single chat box.

How it works

Plan → Delegate → Review → Synthesize.

A business user simply opens a chat and talks to the senior agent — the only one they need to deal with. Behind the scenes:

1

The senior plans the work.

Reads the request, writes a short plan into the chat so you can see it and redirect at any point. No black box.

2

It delegates to specialists.

A drafter, a reviewer, an analyst — each following the firm's standing instructions for its job. Each with its own skill, its own model, its own scope.

3

A reviewer checks the work.

Against the firm's standards, its house positions, its lock-score rules for what counts as settled versus draft. Flags anything off.

4

The senior synthesizes.

Reads the review, resolves issues, pulls the final result together. Every claim traced back to the source it came from.

5

A human approves.

The AI proposes; people decide. There is always a human approval gate before work ships.

You see a clean, readable conversation — the plan, the hand-offs, the issues caught, the final draft with its citations. You never have to manage the specialists. It feels like working with one excellent senior colleague who happens to have a whole team behind them.

What makes it different

Four commitments that make it usable for real work.

Your data stays where it is.

You don't load documents into Agent Manager. You point it at where they already live — your DMS, your data warehouse, your cloud storage — and it keeps an up-to-date map of that knowledge. Your systems remain the single source of truth. Competing products are places you move your data to; Agent Manager is a layer that works over the data you keep.

It learns your house style — with a human in the loop.

Every correction, whether one agent catches another or a partner adds a note in chat, becomes a proposed improvement to the firm's instructions. Once a week, a senior person reviews those proposals on a simple dashboard and approves or rejects each one. Approved lessons become the team's institutional voice on the next job. Nothing changes without a person signing off.

It knows settled from draft.

Agent Manager understands that a signed, executed document is authoritative and a rough draft is not. It quotes the settled ones precisely, treats the unsettled ones as background only, and will tell you if a result leaned on something that hadn't been approved. That distinction is what makes its output trustworthy for high-stakes work.

Supervised, permissioned, on the record.

An agent only ever sees what the person it's helping is allowed to see. Every action it takes is logged for audit. Permissions are re-checked continuously — access revoked mid-task takes effect immediately. There is always a human approval gate before work ships.

One design, many disciplines

Same machinery, different expertise.

The team-of-agents shape fits any work with a recurring request, specialists doing well-defined jobs, a reviewer who catches drift, and institutional precedent worth preserving. Three initial personas — more on request.

Data Engineering
A manager, an engineer, a reviewer, a BI specialist.

A PM asks for a new metric, a dashboard, and an alert. A manager agent decomposes the request; an engineer builds the pipeline against firm conventions; a reviewer checks it for the required tests; a BI specialist builds the dashboard and configures the alert. Ready-to-review work, with firm production rules respected throughout.

See the team
Product Management
A researcher, a PRD writer, a reviewer, a stakeholder communicator.

A CEO asks for a competitive analysis and a launch plan for a new feature. A researcher agent gathers signal from your CRM, roadmap, and support tickets; a PRD writer drafts the spec against the team's standing templates; a reviewer checks against product principles; a communicator drafts the stakeholder update.

See the team
Law Firm
A senior attorney, a drafter, a reviewer.

A partner types “Acme deal — mutual NDA, 2-year term, standard carve-outs.” The senior attorney plans the work, the drafter composes v1 from firm templates and precedents, the reviewer flags two issues against standing positions, the senior returns a clean draft with citations. Save to matter; the redline next day picks up the same conversation.

See the team
Deployment

Three ways to run Agent Manager.

Meet your team's trust requirements where they are. Same product, same skills, same marketplace — different operational surface.

Self-hosted

Available

One Docker Compose stack in your infrastructure.

For the most sensitive environments — nothing leaves your walls. Agent Manager runs entirely on your servers, calls out only to the LLM providers you configure, and stores memory, checkpoints, and transcripts in your database. No shared infra between teams, no external services required for orchestration.

  • Full-stack local deployment — MongoDB, Redis, Celery, FastAPI
  • LLM connections configured per agent — pick your provider per role
  • Marketplace items are importable, no external calls at runtime
  • Volume-bound container has no host filesystem access
Read the install guide

SaaS

Early access

Bring your data — we run the platform.

For teams that want Agent Manager without operating the stack. LeastAction runs in our infrastructure and maps to your document store, warehouse, and cloud storage. Your raw data stays in your systems; the platform keeps a synced, working projection to reason over. Strict per-customer isolation, region-selectable data plane.

  • Your documents and data never leave your systems
  • Region-selectable data plane (US, EU)
  • SSO, per-user audit, RBAC, per-tenant encryption
  • Same skills, memory, and marketplace as self-hosted
Request early access

Managed SaaS

Coming soon

Bring your data + compute — we run it on your cloud.

Your VPC, your storage, your compute — our operations team. Agent Manager runs inside your cloud account (AWS, GCP, or Azure) with a slim control-plane connection for updates and monitoring. Best when data must not leave your account and you want us to carry the pager.

  • Deploys into your VPC — data and compute never leave your account
  • Managed upgrades, patching, backup, monitoring
  • Compliance-ready posture — SOC 2, HIPAA path, per-tenant isolation
  • Break-glass access controls under your key management
Talk to us

Want to see your firm’s team of agents?

30-minute demo. We’ll show the drafting team on a real precedent corpus, or the data engineering team on a real warehouse. Bring one specialist workflow that your team keeps re-doing.

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