Platform

EgentWrX makes AI Agents genuinely productive digital assistants

EgentWrX turns the way your company works into Skills and its past documents into a knowledge base, then connects long-term memory with the workflow systems you already run. Building an Agent takes no long prompts and no pile of settings. Say clearly what you need, and the task becomes an AI task.

7modulesMemory, skills, knowledge, integrations and more

4layersOrganization / department / role / data

3modesCloud, on-premises, hybrid

10,000+Assistants shipped to date

One interface, with every AI Agent capability in it

Agents, memory, knowledge and skills all live in one screen. For people with the background for it, we leave room to configure and tune in depth. Want AI to help with your work without any of that? Tell the Agent what you need and it sets up the task and builds the skill for you. People from any background can pick it up, with very little to learn.

What can Alex do for you?

Available Skills: Email drafting · Inquiry replies · Quote generation · Task creation

Ask Alex anything…

Sonnet

Seven modules, each responsible for one Agent capability

Agents are built by role, so someone with several roles has several of them. Memory is organization-level, not siloed per project. Skills are generated from an SOP in one click and shared company-wide. The knowledge base has three ways to query it, vector, SQL and wiki, and answers cite their sources. Integrations run on the open MCP standard, so Agents reach ERP and email. Routines run on schedule. Nobody has to remember which one to use; just say what you need.

Agents & roles

One role, one Agent

One employee usually wears several hats. Each role can have its own Agent, loaded with multiple Skills and trained for all kinds of tasks; when work crosses departments, it relays with your colleagues' Agents.

One person, many roles; each role gets its own Agent
Each Agent carries multiple Skills and can be trained for new tasks
Across departments, it collaborates and relays with colleagues' Agents

Memory

What is “Memory”?

The more you use it, the better the AI knows your company. Memory lets Agents keep context across conversations and projects, so you never start from zero; and it's organization-level, not siloed per project.

Accumulates across conversations and projects; nothing resets when people or projects change
Organization-level memory: the whole company shares one context
What's remembered and who can access it stays governable and auditable

Routines

What are “Routines”?

Daily reports, reconciliations, inventory reminders: schedule them once and the Agent runs on time and brings you the results.

Set it once; it runs on schedule without being reassigned
Reports back when done and flags anomalies first
Every run is logged and reviewable anytime

Skills

What are “Skills”?

Package your SOPs and your veterans' know-how into Skills. Agents follow them, so new hires are productive on day one. Skills also self-iterate from usage feedback, getting sharper with use.

Turn SOPs into reusable Skills with one click
Build once, share company-wide; update one copy and everyone syncs
Self-iterates from usage feedback, getting sharper with use

Knowledge base

What is the “Knowledge base”?

Knowledge scattered across files, databases, and people's heads becomes a knowledge base your Agents can query. Finding documents, querying data, and asking about rules each get the right method.

Vector semantic search finds documents by meaning, no keyword guessing
SQL queries the database directly, so numbers come with evidence
Policies live in the Wiki, and answers cite their sources

Integrations

What are “Integrations”?

Agents connect directly to your existing systems. They don't just answer, they act: query the ERP, open work orders, send email, schedule, all in one chat box.

ERP, CRM, email, calendar: connect and go
Built on the open standard; new systems plug in anytime
Every action is logged, with permissions controlled by admins

Just ask

One chat box for everything

Nothing new for employees to learn. State the need, drop in the files, Excel, PDF, images, and the Agent decides which capabilities to use and completes the whole job.

Assign work in plain language, no syntax or setup to learn
Drop in files of any format, no conversion needed first
It decides on its own whether to search the knowledge base, use a Skill, or reach a system

Edge computing module (EgentWrX EDGE)

EgentWrX EDGE lets a company decide whether a job runs on the user’s own machine or goes to the company’s server, and it can spread the work across several devices. When a job runs locally, the Agent can read the folders on that machine.

Choose where it runs

You decide whether a given job runs on the user’s computer or goes to the company’s own server.

Local or company serverDecided per job

The Agent reads local folders

When a job runs locally, the Agent can read the folder you point it at on that machine.

Local foldersRead directly

The Agent watches a folder

The Agent keeps watching the folder you specify, and reads any new file as soon as it appears. Nobody has to hand it over.

New filesPicked up automatically

Hand off to another Agent

Once the Agent has read a file, it can pass the work to another Agent.

Agent to AgentHand off after reading

Imagine one complete workflow, handed off between 5 AI Agents across 5 roles until it is finished

Real work is a relay across departments, not one person talking to one chatbot. As a rush order moves through five roles, each agent picks up its own leg.

Point · Sales receives a customer rush order

Emily Carter · Sales Agent

The moment a customer rush order arrives, a first-draft quote is ready

Sales simply asks the Agent to read the customer's email and requirements and check inventory and the latest pricing, and a first-draft quote is produced.

What happenedAn email arrives from Acme Automation with a rush inquiry: hex socket M8×1.25 SUS304, 5,000 pcs, quote needed by Friday.

What the Agent did for youUnderstood the requirements, checked inventory against company pricing rules, and produced a first-draft quote (pending your confirmation).

Sales Agent → confirms capacity and order insertion with the Scheduling Agent

Line · Agent handoff

Mark Reyes · Scheduling Agent

A rush order, automatically slotted into the running production line

The Agent analyzes the potential impact based on capacity and works out an optimized production plan.

What happenedThe Sales Agent passed over a rush order to slot into this week's running production line.

What the Agent did for youSimulated the insertion, estimated the capacity impact, and scheduled header machine No. 3, Wednesday morning shift, pushing the original order back 6 hours (still within the delivery window).

Scheduling Agent → confirms material status with the Procurement Agent

Line · Agent handoff

Daniel Brooks · Procurement Agent

Short on materials? The Agent has already lined up suppliers for you

Checks inventory and supplier lead times in real time, laying out the shortfall and options at once.

What happenedThe Scheduling Agent confirmed the order insertion, requiring materials to be sourced right away.

What the Agent did for youChecked live inventory against supplier lead times. SUS304 wire rod is short by 1,800 pcs. Locked in two suppliers (Supplier A, 5 days / Supplier B, 9 days).

Procurement Agent → prepares incoming inspection with the QA Agent

Line · Mandatory gate

Laura Bennett · QA Agent

Special characteristics, released only after each item passes inspection

Compares drawings against measured values, confirming each critical dimension one by one.

What happenedThe Procurement Agent has the materials in place. This batch must pass special-characteristic inspection before shipping.

What the Agent did for youCompared drawings against measurements. Thread major diameter, concentricity, and under-head radius: all three special characteristics passed.

QA Agent → notifies the Plant Manager Agent that the full chain is complete and awaiting approval

Plane · The manager's Agent

Tom Novak · Plant Manager Agent

The manager's Agent watches over the whole plant and release decisions for you

Reviews team Agents' execution, keeping production, cost, and approval-to-release in hand, with the whole process auditable.

What happenedThe work order reaches the final gate awaiting approval, while the plant manager wants a handle on today's plant-wide production, order insertions, and costs.

What the Agent did for youThe plant manager's Agent consolidates each function Agent's progress and exceptions, compares delivery dates against costs, flags the decision points, and compiles an approval sheet with an audit trail, ready for your one-click release.

Manager reviews team Agents → approves release → every step is logged and auditable

What an enterprise needs is governance, not a chatbot

Personal productivity tools are strong. What stops enterprises is different: can the data stay on site, can permissions be layered, can every step be traced?

Layered permissions

Control who can use and see what across organization, department, role and data, so least privilege actually holds.

4Layers of RBAC

Full audit trail

Every call, every knowledge lookup, every write back to a system is logged, so you can answer who did what and when.

End to endEverything logged

You choose where data sits

Cloud, on-premises or hybrid. Sensitive data stays inside while inference still reaches frontier models.

3Architectures

Identity governance

Connects to your existing AD and SSO. One account, revoked on the day someone leaves, with no separate user list to maintain.

AD / SSOUses your directory

Compared with other AI work platforms

They are all strong at personal productivity. The enterprise question is different: can data stay on site, can permissions be layered, how do agents hand off across departments?

	EgentWrXIntellicon	ChatGPT WorkOpenAI	Claude CoworkAnthropic	Microsoft CopilotMicrosoft
Multiple modelsNo lock-in to one vendor. Pick the model that fits the task, the budget and the compliance requirement, and survive the next model generation without a rebuild.	✓Native multi-model	✕OpenAI models only	✕Claude models only	✓GPT / Claude / Phi routing
Layered permissions ()Control who can use and see what by department, role, agent and data, to satisfy least privilege and audit.	✓Organization / department / role / data	◐Enterprise group permissions	◐Enterprise custom roles	✓Entra + Agent 365
Single sign-onOne account, revoked on departure, the baseline for security compliance. On-premises also has to reach your existing AD or LDAP.	✓Includes on-premises	✓ + SCIM	✓SAML / OIDC (enterprise)	✓Native Entra ID
Knowledge baseTurn what sits in people’s heads, files and databases into an organizational asset: vectors for semantic search, SQL for structured data, a wiki for rules. Governable, searchable, and it does not leave when someone does.	✓	◐Connector RAG retrieval	◐Files / project memory	✓Graph / Dataverse
Hybrid deploymentIn manufacturing, finance and government, data often cannot leave the country or reach a public cloud. On-premises and private cloud are hard requirements.	✓Cloud + on-premises / private cloud	✕Cloud only	◐Customer cloud or self-hosted gateway	◐Local processing in preview
Cross-department workflowsReal work is a relay (sales → scheduling → purchasing → quality → plant manager). A single assistant cannot cover the handoffs between departments.	✓Native agent-to-agent relay and notifications	◐Requires AgentKit / SDK	◐Subagents / managed agents	✓Multi-agent A2A coordination
One-click skillsTurn SOPs and expertise into reusable skills without going through an engineering team every time.	✓One click	◐GPTs / AgentKit	✓Skills framework	◐Built in Copilot Studio