Task orchestration
The lead agent clarifies the goal, breaks the work down, routes it to specialised roles and assembles a verifiable result.
Applied AI · Digital agents · Control
AI that's
embedded in the work
Not a standalone chat and not a technology demo. A system that works with data, knowledge and tasks, cuts manual overhead and keeps humans in control where it matters.
4linked compute nodes
8working roles in a single system
Dailyreports, checks and backup cycles
Principle
Autonomy is only useful when it delivers real results and doesn't undermine manageability.
Confirmed projects
Real AI cases instead of a generic capability list
6 TRACKSAll applied casesWorking systems, prototypes and products →PROTOTYPE AND MVPAI Service PrototypingCheck quality and economics before deployment →CONTENTPublication AutomationSocial media, websites and video through one agent →FINANCE AND KNOWLEDGEIslamic finance and lawKnowledge bases and AI consultant →MODEL SELECTIONModels and their rolesLocal models, quality gates and cloud escalation →LOCAL AI OPSLocal AI operationsFree issue and practical deployment guidance →ROI TOOLLocal AI economics calculatorCompare local infrastructure with API and cloud →
Memory and search
QMD-compatible knowledge base and local RAG return answers with sources and persist accepted decisions.
Content and research
Research with sources, articles, posts, scripts and visual briefs are produced as verifiable drafts.
Engineering and visual
Separate workflows for code, testing, infrastructure and images; publishing and risky changes require approval.
Applied scenarios
From a management question to data and a pilot
Live project
Digi Anton
A practical multi-agent system combining local AI models, knowledge bases, tasks, reports, content, infrastructure and controlled autonomy.
Orchestration — separate agent roles and contexts.
Memory — long-term QMD-compatible knowledge base.
Automation — recurring tasks, reports and workflows.
Control — approval before publishing and risky actions.
Reliability — backups and health-checks.
Project architecture
Different roles.
Shared memory.
Controlled actions.
01Anton and channelsgoal · context · confirmation
↓
02 · CONTROL LAYEROrchestration platformrouting · queue · reports
03Specialised agentscontent · knowledge · analytics · code · visual
04QMD / RAG memorydecisions · documents · search with sources
↓
05 · COMPUTE LAYERSpecialised compute nodeslocal models · analytics · programming · visual tasks
What works now
An observable system, not a promise
Status of roles, queues, errors, models, memory and backups.
Read-only RAG with source citations; fast mode for operational status.
Local copies and mutual replication of core systems across compute nodes.
Orchestrator, knowledge manager, content planner, business analyst, visual worker, compute operator, programmer team and personal secretary.
Autonomy levels
Access scope determines autonomy level
Operates within approved workflows
Local search, reports, health-checks, task routing, material preparation and publishing through connected project connectors.
Confirmed by a person
New channels, sensitive content, personal planning, code changes and actions outside approved scenarios.
Connected as a separate project
Email, calendar, voice channels and other external actions are enabled only after access setup, a control scenario and quality check.
How a solution is built
Task → context → prototype → working system
Pick the routine
Find a process where automation genuinely saves time.
Gather context
Sources, rules, data, roles and constraints.
Test the prototype
Quality, cost, speed and edge cases.
Bake in control
Logs, approval, redundancy and rollback.
AI without the noise
Start with one useful task
Describe the manual process that regularly eats time.
Write about your AI task →