Put AI to work. Keep it on a leash.
Build AI agents on a visual canvas and let them act across your systems: drafting, updating, triaging, deciding. They run on a box you own, so no data leaves the building, every consequential step waits for your approval, and automating more never costs you more per task.
Click to zoom
Trigger, reason, act. Without losing the plot.
Agents lets you build automations on a visual canvas: drag a trigger, your tools, your data, and your private models onto the board and wire them into a working agent, no code required. Each agent reasons with your own models, grounded in your own knowledge, and then does something real in the systems your work already lives in. Every step runs on the appliance, so however much you automate, the cost does not climb and the data does not move.
Trigger
Something happens. A new ticket, an inbound invoice, a schedule, an event in a connected system, or a manual run sets the agent off.
Reason
The agent decides. It classifies, extracts, or plans using your local models, grounded in your knowledge buckets, and works out what to do next.
Act, on your terms
It drafts the reply, updates the record, files the ticket, with a human in the loop wherever the step is one you would not let run unattended.
A demo agent is easy. One you can trust is not.
Anyone can show an agent doing something clever once. The hard part is an agent you can hand real work to: one that recovers when something breaks, asks before it does anything irreversible, and leaves a record you can check. That is what Agents is built for, so you are deploying a dependable colleague, not a liability.
Durable by design
Long-running jobs survive restarts and resume exactly where they left off. A task that takes minutes, hours, or days does not lose its place when something hiccups, which is the difference between a real platform and a script that dies quietly at 2am.
Human in the loop
Pause any agent for a person to approve an action before it happens or review an output before it goes out. The agent waits for the call, then carries on. You decide exactly which steps are too consequential to run on their own.
Full memory and state
Agents hold context within a task and remember across runs, so long, multi-step work stays coherent instead of starting from zero every time.
Replay and audit
Every step an agent took is recorded and can be replayed, so when someone asks why it did what it did, you can show them, not guess. That trail is what makes automation defensible.
You do not start from a blank canvas.
Begin from a template that already wires up a common workflow, then adapt it to your systems.
Ticket triage
Classify inbound tickets, enrich from CRM, and route or draft a reply, grounded in your docs.
Invoice intake
Extract line items from incoming invoices, match POs, and flag exceptions for approval.
Meeting notes
Summarise transcripts into decisions and action items, then post to the right channel.
RFP drafting
Answer RFP questionnaires from your approved knowledge base, ready for a human to review.
Research brief
Gather across buckets and produce a sourced brief on any internal topic, on demand.
Inbox sorter
Categorise a shared inbox, extract requests, and create tasks in your tracker automatically.
Free field report
Before you decide on AI, read the honest landscape.
Twenty pages on where AI actually cuts cost or does more with fewer people, and how to make a first move that pays for itself.
20 ppThe more you automate, the more cloud agents cost you. Yours run for free.
Every agent step on a metered cloud is another charge, so the more useful your automation becomes, the bigger the bill. On the appliance, agents run on hardware you own: scale to a hundred agents or a hundred thousand runs and the cost does not move. And like everything in the box, they run on your network, so wherever you operate, your data, your prompts, and your models stay inside your walls. Data-protection rules are tightening worldwide, from GDPR in Europe to India's DPDP, Brazil's LGPD, and the Gulf's residency regimes, and they all point the same way: keep control of your own data.
Turnkey on the appliance
Agents arrives installed and ready on hardware you own. Connect power and network and you are building your first agent in minutes, with no setup project to staff.
One private console
Install, update, and switch the open models your agents use from one console, no command line. You move as the field moves, on your schedule, not a vendor's.
Observability built in
See what every agent is doing, where time goes, and where a run failed, all from one place, so running a fleet of agents stays manageable instead of mysterious.
Access governed by buckets
Agents read and write only inside the buckets and permissions you grant, so an automation respects exactly the same access rules as the people it works alongside. An agent can never reach what its owner could not.
Where the queue never gets shorter on its own.
Agents earns its place wherever repetitive, judgement-heavy work piles up faster than people can clear it.
Operations
Triage incoming tickets and requests, route them, and draft first responses, so the queue is sorted and started before a person even opens it.
Finance
Read inbound invoices, pull the line items, check them against your records, and stage them for approval, with a human confirming before anything is posted.
Sales and bids
Turn an RFP into a structured first draft grounded in your own past proposals and product knowledge, on-premise, so confidential commercial material never leaves the building.
Knowledge work
Summarise meetings, compile research briefs, and sort inboxes against your own context, handing people back the hours the busywork was taking.
Your models. Your box. Your rules.
Agents stays open where it matters and private everywhere: run the models you choose, manage them in a click, and keep every byte on the appliance.
Open models, your choice
Run the open models you trust and swap them freely as the field moves, with no lock-in to a single vendor or model.
One-click management
Install, update, and switch local models from the console, no command line, so staying current is a click, not a project.
Everything stays on the box
Whatever you run, your data, documents, and models never leave the appliance. That is the foundation of private, sovereign AI.
About Malogica Agents.
No. You build on a visual canvas: drag triggers, models, tools, and conditions and connect them, no code. Start from a template and adapt it, so the person who understands the process is the one who can automate it.
Everything runs on the appliance, on your own network. Your data, prompts, and models never leave the building, which makes this private, on-premise automation, not a cloud service wearing a privacy badge.
Built-in connectors and a standard integration let agents read and write to the tools your work already lives in: tickets, email, chat, records, files, and more. An agent takes real action, not just produces text.
Yes, and you decide which ones. Pause any agent for a person to approve an action before it runs or review an output before it is sent; the agent waits, then resumes. You keep control of every consequential step.
It resumes exactly where it left off. Long-running agents are durable, so a task that takes minutes or days survives a restart instead of dying silently and leaving you to notice later.
Yes, completely. Every step is recorded and can be replayed, and a console shows what each agent is doing, where time goes, and where a run failed. You can always show the why, not guess it.
Nothing extra. Every step runs on hardware you own, so automating more work never raises a bill, which is the exact opposite of metered cloud agents, where success is what makes them expensive.
The open models you choose, managed from one console and swappable freely, with no lock-in to any single model or vendor.
Because agents run on hardware you own, your data is never processed by a third party and never crosses a border you did not choose. That holds wherever you operate, under GDPR, DPDP, LGPD, Gulf residency rules, or sector regulations.
Agents read and write only inside the buckets and permissions you grant, so an automation respects the same access rules as the people beside it. An agent can never reach what its owner could not.
A new ticket, an inbound message, a schedule, an event in a connected system, or a manual run. You set the trigger on the canvas, so an agent fires exactly when your process needs it.
Book a demo and we will show agents running against real systems on the appliance, then help you size the configuration for the workloads you want to automate.
Watch an agent clear real work, on-premise.
Book a demo and we will run agents against real systems on the appliance, with you deciding what they are allowed to touch.