Hábito 1 runs its own website with a roster of agents

Hábito 1 built H6 Orchestra to organize teams of AI agents. The first operation where it had to be proven was our own.

An orchestration framework can be described very well in a presentation. What cannot be simulated is sustaining it every day over a real operation, where mistakes cost and nobody fixes them on the next slide.

And a technology company website is a demanding test bed.

• Several fronts at once: search positioning, content, case studies, commercial pages and metrics, each with its own rhythm.
• Fronts that overlap: two agents writing about the same term compete with each other without knowing it.
• State that evaporates: if every session rebuilds where things stood, work gets repeated and nobody can audit it.
• Nobody watching from above: in an autonomous operation, the control has to live inside the system.

If the method does not hold at home, there is no point offering it outside.

We deployed the framework over our own digital assets and run it like any client: one agent per front, each with its contract, its actionable state and its log.

There is no special internal version. The rules governing this operation are the ones delivered in an implementation.

One agent per front

• Search positioning: auditing, term research, opportunity decisions and measurement over our own properties.
• Case studies: source research, writing against policy, loading into the content manager and verification.
• Pages and commercial landings: structure and publishing with their own criteria.
• Business intelligence: data modeling, metric definitions and dashboard building.
• Each front has its own repository, its own policy and written limits, so none can write where it does not belong.

Actionable state and log, deliberately separate

• The state says what needs doing now and wins against any contradiction with another file.
• The log records what happened. It is history, not state, and confusing the two is what makes an operation repeat work.
• Every session opens by reading its state, in a declared order and with a maximum reading budget.

Gates that actually stop the work

• Automatic verification of the writing and loading policy before publishing.
• Cannibalization detection across our own pieces, so two fronts do not compete for the same term.
• Explicit human approval on strategic publishing decisions.
• And shared files protected by an exclusive window: two agents working in parallel on the same file overwrite each other without any error, so the system blocks it beforehand.

Controls tested by breaking what they watch

• Case study verification runs over more than a hundred controls, and each control has its own test confirming it fails when it should.
• It is the central practice of the method: a control that does not measure looks identical to one that does.
• Several of those controls were born from real errors in our own operation, and each one kept the case that caused it noted beside it.

Architecture and technology

• One front per discipline, with declarative identity, state, log and its own policy.
• Direct connection to the content manager and to the analytics flows.
• Executable verification suites and a concurrency lock over shared files.

Learn more about AI agents, digital marketing and software development.

The company digital operation runs on agents in a sustained way, and that is what backs every claim we make about the method.

What sustains the operation

• A roster of fronts specialized in search positioning, content, case studies, pages and metrics.
• More than 100 case studies researched, written, loaded, published in 2 languages and verified.
• A suite of more than a hundred policy controls, each with its own test proving it fails when it should.
• More than a hundred internal links distributed across the service pages, decided by criteria and not by volume.
• An auditable log and an actionable state kept separate in every front.

What shows up when you really operate

• The controls caught errors made by the agents themselves before they reached production, which is exactly why they exist.
• And several were added after a real failure, with the case noted beside them so the reason is not lost.
• That is the difference between a policy written once and one corrected through use.

Why it matters

• It is the in house validation of the method we offer: there is no demo version and a real one.
• And the seven phases of an implementation are the same path we walked to restructure this operation.