Augmentoring · AI agents for real workflows

AI agents that move real work forward.

Augmentoring builds autonomous assistants for clearly defined workflows, integrated into your systems with controlled access and visible outcomes.

Request a potential assessmentCheck fit in 3 questions

Get a clear first assessment of value, feasibility and the most useful next step, with a grounded recommendation for the right level of automation.

INSPR
Technology foundation

People and AI agents work under the same clear operating rules.

INSPR is not a monolithic platform. It is a shared operating model where versioned rules, task-specific context and declarative state keep responsibilities and outcomes legible across people, agents and tools.

  • Versioned agent rules
  • Task-specific context
  • Declarative state

A shared foundation keeps agents, applications and operations connected, even when models, tools or responsibilities change.

Learn more about INSPR(opens in a new tab)

From intent to operations

Every step leaves a verifiable handoff.

Handoff 01

Defined workflow

A concrete workflow, desired outcome and accountable owner are defined before any technology choice.

OutcomeVerifiable intent

From conversation to outcome

A chat window does not change a process.

Value emerges when AI receives the right context, uses only approved tools under control, understands state and returns outcomes to the workflow. Augmentoring builds that connection.

Shared starting pointIntent
  1. Intent and exception paths

    Agentic workflows

    The workflow knows its goal, steps, wait states and abort paths, not just a single answer.

    Progress and exceptions remain transparent and visible.
  2. Selected context and tools

    Integration, not isolation

    The agent works only with approved systems and data required for this specific intent.

    Access remains explainable within the work context.
  3. State, approval and evidence

    Operational control

    People can see what is running, what is waiting and which decision is needed next.

    The documented outcome returns to the process.

Strong starting points

Start where impact becomes visible.

The best starting point is a clearly defined workflow with visible value. Depending on the task, that may lead to process improvement, conventional automation or an accountably operated AI agent.

  1. Starting point 01

    Accelerate preparation reliably

    Starting point
    Approved information lives across multiple sources and must be assembled again for each case.
    Approach
    A defined workflow combines the agreed sources and prepares a decision-ready overview.
    Value
    Sources, selection criteria and outcome are documented directly with the case.
  2. Starting point 02

    Make handoffs continuous

    Starting point
    Handoffs connect people, tools and agent runs and need reliable shared context.
    Approach
    Every handoff includes selected context, clear ownership and a defined return path.
    Value
    Status, review and decision form one continuous, auditable trail.
  3. Starting point 03

    Focus operational decisions

    Starting point
    Multiple operational signals compete for classification, priority and a clear next step.
    Approach
    An agent organizes known signals and prepares reviewable options for the responsible person.
    Value
    Priority, decision and next step are documented together.
  4. Starting point 04

    Govern automation responsibly

    Starting point
    Automated workflows connect permissions, approvals, safe stops and recovery.
    Approach
    Capabilities, approval points, stops and restart paths are clearly defined for every execution.
    Value
    Actions, approvals and resumption remain assigned to an accountable workflow.

Do you have a concrete workflow?

Together we make the starting point, suitable approach and success criterion concrete.
Discuss a concrete workflow

INSPR technology family

One foundation.
Clear responsibilities.

Augmentoring is your implementation partner. INSPR provides the technology foundation; Paimos, Pharos and Janus own clearly defined operational responsibilities.

Hover or focus a product to highlight its responsibility in the system.

Technology foundationINSPR

Operating foundation for agents, applications and operations

Agent doctrine constrains agent behavior
Project context

Paimos

Issues, linked repositories, project knowledge, execution choices and run evidence remain connected to the relevant work context.

Project context scopes agent work
  • Work, planning and delivery status
  • Repository and project knowledge
  • Agent runs with evidence
Fleet state and backup evidence

Pharos

Host state, onboarding, configuration drift, backup evidence and approval-gated maintenance become visible in one calm fleet view.

Approvals govern host actions
  • Health signals and drift
  • Backup and restore evidence
  • Approval-gated actions
Secret governance

Janus

References, policy checks and permitted execution stay separate; oversight and audit evidence require no secret values.

Roles govern secret access
  • Opaque secret references
  • Short-lived, purpose-bound permits
  • Role and audit evidence

Product status at a glance

Built, running and moving forward.

Four building blocks with clear responsibilities, from a shared operating model to governed secret use. Version, scope and source remain directly inspectable.

In production, self-hostedINSPRContinuously maintained

Shared operating model for versioned agent rules, task-specific context and declarative operational state.

Current scope

  • Versioned agent rules
  • Task-specific context
  • Declarative operational state
In productionPaimosCurrent 4.x release line

Self-hosted project platform with web interface, API and CLI for people and AI agents.

Current scope

  • Issues and delivery status
  • Repository and project knowledge
  • Agent runs with evidence
Active early releasePharosActive 0.1.x release line

Active early release with live fleet view, host onboarding and controlled operational actions.

Current scope

  • Live fleet and backup evidence
  • Host and provider onboarding
  • Approved actions and host removal
Governance live, self-hostedJanusRust 0.1.x · Go oversight

Governance and audit run live. The Rust core connects reference-based MCP with approvals, verified execution and lifecycles.

Current scope

  • Roles and oversight
  • Approvals and execution
  • Lifecycle and evidence

Start deliberately

From bottleneck to productive solution.

We invest in clarity first. Technology follows only once intent, ownership and a verifiable outcome are defined.

INSPR: Agent doctrine constrains agent behavior
Paimos: Project context scopes agent work
Pharos: Approvals govern host actions
Janus: Roles govern secret access

Accountable autonomy

Autonomy starts with boundaries.

A productive agent needs both a clear intent and clear stopping points. For every workflow we define access, approvals, logging, failure paths and human accountability.

  • Only the access the intent actually requires.
  • Approval before high-impact steps.
  • Visible state instead of invisible background automation.
  • Traceable actions and defined recovery.

Hosting, privacy and data residency are resolved concretely during assessment, not claimed in broad terms.

First orientation

Is your process ready for an AI agent?

Three questions are enough for an initial assessment. The result stays in your browser and is not stored.

01 Is there a clearly named, recurring workflow?
02 Are the required information and systems accessible in principle?
03 Can someone own the outcome, risk and approvals?

Your potential assessment

Which recurring workflow is slowing your team down?

Describe the workflow in a few sentences. Together we will determine whether an AI agent, conventional automation or a process change is the best fit.

  • Fit and appropriate level of automation
  • Risks, access and required approvals
  • Next step and realistic integration path

Bullet points are enough. All fields are required.

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