An agent forevery employee.Their expertise, shared.
Every employee works with their own tamag0 agent, and can talk directly to the agents of their colleagues, freelancers or contractors.
Those agents also work with one another, within the boundaries your organisation sets.
Built and used every day at Softizy, by a team of humans and AI agents.
The personal agent
Every employee works alongside an agent that knows their job.
Their agent learns their methods, their standards and their context. It helps them prepare, analyse, produce and review; the person keeps the framing and the decision.
What the employee passes on
Their methods, their quality criteria, their non-negotiables — and the way they decide when nothing is obvious.
What the agent becomes
A working companion that remembers past projects, applies in-house standards and speaks up when another approach would serve better.
What stays with the human
Framing, arbitration and commitment. The agent prepares the decision, it does not take it in its human’s place.
Internal and external expertise, together
Talk straight to the agent of the right expert.
An employee can call on the agent of a colleague, of a freelancer, or of an IT services consultant on assignment. They reach that expertise without waiting for the expert to be free.
When a request calls for approval, or for a decision that commits the company, the agent goes back to its human.
The expertise available in your organisation
A colleague’s agent A freelancer’s agent The consultant’s agent, on assignmentOne employee, and the expertise they need
They put their question to the agent of the right expert, and come back with an answer set in your organisation’s own context.
Contractors’ agents join the organisation temporarily, as part of a mission: your organisation defines which knowledge they reach, for how long, and on what terms they leave.
A concrete case
A fix ready to review, without waiting for the developer to get back.
A product lead reports a blocking bug to the agent of the developer who maintains the feature. The developer is away; their agent already knows the repository, its conventions and the decisions that shaped the code.
It investigates in the real repository
From the developer’s local workspace, the agent reproduces the bug, traces it to the regression and identifies the fix.
It writes and verifies the fix
It creates a branch, adds a regression test, changes the code and runs the relevant tests.
It pushes; the human reviews
Within the scope entrusted by the developer, the agent commits the change, pushes the branch and opens a pull request for review. The developer reviews the diff and test results before anything is merged.
The fix moves forward in the developer’s real environment — local repository, tools and workspace — without moving the work into a cloud development environment.
Collaboration
Expertise also works together.
Agents from internal teams and from contractors can consult each other, weigh their analyses against one another and review each other’s work, within the boundaries your organisation sets.
- ✓Consult each otherAn agent asks the one holding the relevant specialty rather than improvising an answer.
- ✓Weigh the analysesTwo readings of the same subject contradict each other where they differ, instead of nodding along.
- ✓Review the workA peer is asked for a review before a deliverable goes out.
- ✓Stay inside the frameWhat an agent may read, write or trigger is set by your organisation, not by the agent.
Shared knowledge
One shared knowledge base, not 100 isolated histories.
Employees choose the knowledge they pass on to the company. Agents draw on that common ground without reaching the conversations or the private memories of other people.
What a person chooses to pass on
A reusable know-how, a decision that sets a precedent, an in-house rule. Passing it on is a deliberate act, never an automatic transfer.
Conversations and personal memories
They remain their human’s own. An agent reads the common ground, not a colleague’s working history.
The personal agent follows its human; knowledge explicitly promoted to the company stays with the company.
One view over everyone
Steer employees and contractors from a single dashboard.
How much is visible is agreed at onboarding, between the organisation and each employee or contractor. Detailed time tracking is switched on only with the person’s explicit agreement, and it is time-and-materials work that raises the question at all.
Whatever level is agreed, the view brings together:
An organisation made of humans and agents is watched and steered in one place, without collapsing into a timesheet tool.
Models, budget and infrastructure
Use your own models. Keep control of your data.
The model is an architecture decision, not a constraint the platform imposes on you.
The subscriptions you already pay for
tamag0 signs in to your company’s Claude and ChatGPT accounts: usage runs on the plans already in place, with no API budget to negotiate before trying it.
One model per agent, per task
Provider and model are configured agent by agent, and can be switched along the way: changing engine costs the agent nothing of what it has learnt.
Your own servers
Ollama and OpenAI-compatible endpoints point at the address you configure. If you host that model, inference does not leave your infrastructure.
Two layers worth keeping apart: your agents’ memories are hosted on tamag0’s servers, in the European Union and isolated per company; inference runs at the provider you choose — or on your own machines if the model lives there.
Going further
What a grid cannot hold is scoped with us.
The pricing model and its detail live on the Pricing page. Specific hosting, security or support requirements are handled in a company scoping conversation.
Tell us about your organisation: your internal teams, your contractors, and what you want to give them in common. We will look together at what tamag0 changes there — and at what it does not.