Aigensei vs Enhans (2026)

Through Model Context Protocol the platform connects to any live data source, including CRMs, knowledge bases, ticketing systems, pricing engines and ERP systems. Workflows can call any tool, including RAG knowledge bases, MCP servers connected to live systems, or external agents.

Coverage comes from the protocol, not a fixed connector catalog, and the platform acts on real data in real time instead of cached answers. No individual integration product is named, and there is no connector catalog, count or configuration guide.

Live integrations already run inside partner products, including a partner CMS, a customer support platform and a behavioral health product.

Sourceaigensei.ai platform and home pagesread 2026-08-31

There is no named third party system, such as an ERP, CRM, marketplace, data warehouse or collaboration tool. Data comes in by category, from databases, APIs and files, and Enhans names industries instead of systems.

Agents take authenticated action in outside systems through ACT-2, which operates browsers, SaaS platforms, admin panels and operational portals. Pipeline Builder connects and prepares enterprise data, bringing databases, APIs and files in so the data is ready for execution, and it reads from those sources.

App Builder sends results to dashboards and views inside the platform, not to an outside system. There is no named integration catalog and no support for custom tools. Agent Builder connects agents with tools, rules and conditions but names no tool, says nothing about how one is added and has no tool interface.

Enhans drives interfaces instead of publishing connectors, because many enterprise systems do not expose complete APIs.

Sourceenhans.ai/agent-os Pipeline Builder and Agent Builder sections, enhans.ai/act-2 API-limited system coverageread 2026-09-14

Each request runs through a seven step execution architecture, starting with request intake. A planning agent then interprets intent and designs the execution path, deciding which workflows apply, what information is needed and how to sequence the steps. Workflows configured in advance take control, with defined steps, conditional logic and decision branches. They call tools such as RAG knowledge bases, MCP servers or external agents, and their results go into a shared memory layer.

A specialized Writer Agent formats the output in the correct tone, structure and brand voice, and the loop closes by delivering a response or downstream action. The process uses three specialized agent roles. One configuration handles thousands of parallel process instances. Aigensei says nothing about looping, parallel steps within one process, or failure and retry handling.

Sourceaigensei.ai platform and home pagesread 2026-08-31

AgentOS runs work in several steps across several agents. Five modules sit in order on one execution path. Pipeline Builder prepares data, and Ontology Manager defines business meaning and relationships. Agent Builder designs agents and execution flows, and App Builder connects business meanings to operational interfaces. ACT-2 then carries out the business work. Enhans describes AgentOS as connecting "enterprise data, ontology, agents, workflows, views, and action in one execution architecture," with each layer made explicit.

Enhans presents running several agents together as a differentiator. AgentOS lets multi agent systems operate according to enterprise logic, so enterprises can build and connect agents for specific business purposes that run within real workflows and operating logic. In the market monitoring use case, monitoring agents run over Pipeline Builder, ontology management and workflow logic, with Price Agent, Social Media Agent and Brand Protection Agent as the example agents on one flow.

The customer composes the flows. In Agent Builder, a customer builds task specific agents on top of enterprise data and ontology, then connects them with tools, rules and conditions into workflows for real operations. The customer defines how agents operate, what information they can use, when actions fire and how exceptions are handled. Rules and conditions written for each flow give the customer control over branching.

Enhans names no runtime, BPMN or graph notation, versioning, retry or concurrency behavior, and does not say what happens when a step fails in a flow with several agents.

Sourceenhans.ai/agent-os architecture layers, differentiators and use cases, FAQ Q5read 2026-09-14

Every process enters at request intake, the first step of the execution architecture. A form submission, an inbound message, an API trigger or a scheduled event can each start one. Inbound messages also arrive through an embedded widget inside a partner's customer support product, where the platform runs live. There is no webhook configuration or event subscription mechanism, and no cron or recurrence syntax for scheduling.

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In Agent Builder a customer defines when actions should be triggered, alongside how agents operate, what information they can use and how exceptions are handled. With trigger conditions written for each agent, the platform runs on events instead of by hand.

In the market monitoring use case, monitoring agents watch market signals in real time and connect insights to an operational response. The use case is built with Pipeline Builder, ontology management, workflow logic and dashboard views.

Enhans does not say what can fire a trigger. It names no event source, such as a webhook, schedule or inbound API call, a message queue or subscription to a system's change events, or a mailbox, chat channel or ticket queue. Outputs go to dashboards, generated views and natural language interfaces.

Sourceenhans.ai/agent-os Agent Builder FAQ and market monitoring use caseread 2026-09-14

RAG knowledge bases are a first class tool that workflows call in step four of the architecture, alongside MCP servers connected to live systems and external agents. The platform acts on real data in real time, not cached answers or information that was true yesterday. That gives it both a persistent retrieval corpus and live queries into CRMs, ticketing systems, pricing engines and ERP.

Retrieval follows an explicit plan, with the planning agent deciding what information is needed before workflows retrieve it. Aigensei does not say how a knowledge base is created, what formats it ingests, how it is indexed or refreshed, whether answers carry citations, or who maintains it.

Sourceaigensei.ai platform and home pagesread 2026-08-31

Ontology Manager, a product module, turns data into business meaning as a graph. It defines entities, relationships, rules and terminology so AI can work with real enterprise context. Together these form a semantic layer that agents query, instead of context assembled for each run.

The layer persists. AgentOS builds a separate ontology lake while keeping existing systems in place, using Pipeline Builder to store the required data separately and organize it for AI instead of modifying enterprise databases directly. Pipeline Builder fills it from databases, APIs and files, and agents query it at run time. Because it is a store and not a prompt, it scales past a context window.

Enhans argues that ontology gives AI the structure it needs to work with enterprise context. Without it, AI may generate plausible responses, but it is far harder to make them consistent, explainable and operationally useful. The structure is maintained, not built once. Ontology Manager generates about ninety percent of the ontology automatically from source system data, and forward deployed engineers complete the rest on site.

The ontology describes the customer's business, not state an agent keeps about its own work. Enhans does not say how agents query the ontology or what provenance an answer carries, and names no retrieval mechanism, query interface, embedding approach or citation surface.

Sourceenhans.ai/agent-os Ontology Manager, ontology lake and FAQ Q3read 2026-09-14

A shared memory layer stores all results, retrieved data and decisions so every later step has full situational awareness. Shared memory across all workflow steps gives each agent full context of the session. That memory lasts for a single process run across the seven step loop, persisting across steps but not across sessions.

State does not carry past the closed loop, and one configuration runs thousands of independent parallel process instances. Aigensei also says the platform learns from outcomes over time and improves as it operates without manual retraining, but it names no store, retention policy, per customer memory or mechanism behind that.

Sourceaigensei.ai platform pageread 2026-08-31

Agents have no memory store, and Enhans names no scope, lifetime, retention period or purge path. A customer cannot find where agent memory lives or delete it for one customer without touching the ontology lake, which is the application's data model. There is also no store of facts that users maintain for the agent to consult.

The persistent ontology describes the customer's business, its entities, relationships, rules and terminology, held in a separate ontology lake. Agents do not write state about their own work into it and read it back. The platform learns continuously across cycles, and that learning is absorbed into the product.

ACT-2's Reuse step saves successful action paths and applies them to similar future tasks. Its reusable workflow execution converts successful task paths into workflows that can be used again, reducing the need to reason through the same workflow on every run. The agent writes something lasting from its own experience, but it becomes a procedure the platform replays, not context the agent reads when it decides.

Sourceenhans.ai/act-2 reuse step and reusable workflow execution, enhans.ai/agent-os ontology layerread 2026-09-14

Precision human escalation hands work to a person with full context, with escalation thresholds set by workflow, segment or risk. Every process runs within boundaries the organization defines, and the platform decides when to bring a person in. There is no runtime approval step, checkpoint, pause for confirmation, queue of pending actions or reviewer approval surface. Execution closes with a delivered response or downstream action. Approval flows come as an Operations and Approvals playbook that customers configure, not as a platform primitive.

Sourceaigensei.ai platform and home pagesread 2026-08-31

Decisions stay with the customer, and the tagline is "you decide and our agents act." In Agent Builder the customer sets limits on agent autonomy at build time, defining how agents should operate, what information they can use, when actions should be triggered and how exceptions should be handled. ACT-2 runs through a "governed execution layer," so AI agents can take action without the customer giving up control over the execution environment. There is no step where a person reviews and approves an action.

There is no approval queue, pending state or accept and reject action. There is no reviewer, risk threshold per action or escalation path to a named person, and no record of who approved what. Enhans does not say whether a person handles exceptions, and ACT-2's Adapt step runs fallback execution with no human in it. The execution layer is built to reduce human involvement, cutting the need to reason through the same workflow on every run so that high volume automation becomes more economically viable.

Sourceenhans.ai/agent-os Agent Builder FAQ, enhans.ai/act-2 governed execution and adapt stepread 2026-09-14

The infrastructure is aligned with SOC 2, but Aigensei claims no audited attestation. It names no SOC 2 type, observation period or audit firm, and has no trust page or way to request a report. Customer facing controls include role based access controls with granular permissions at every level of the organization, data residency controls, full audit trails that log every AI action and the full decision chain, and explainable decisions. Aigensei targets healthcare, financial services and insurance and treats security as a design constraint.

Sourceaigensei.ai platform and home pagesread 2026-08-31

There is no security attestation or access model for the platform. AgentOS can be deployed to match customer infrastructure and security requirements, and Enhans says enterprise AI must account for existing systems, security and infrastructure requirements. Both are about fitting what the customer already has, and neither says what Enhans itself does.

There is no SOC 2, ISO 27001, auditor, report, trust center or compliance page. Enhans names no SSO, SAML or SCIM, and no RBAC, roles or permissions model. The site has no security, trust or compliance section, and its only legal link is a privacy policy.

Enhans sells to large enterprises and defense customers, including through the Azure Marketplace.

Sourceenhans.ai/agent-os deployment section and full navigation, enhans.ai/act-2 navigation and footerread 2026-09-14

Every action, decision and branch is logged, traceable and explainable, with no black boxes and no gaps in the record. Full audit trails cover every AI action and the full decision chain, not just outcomes. The execution architecture is built to close a business process with every decision logged, every action traceable and every escalation precise. Explainable decisions are a separate property alongside logging, meant for a compliance team asking what the AI did and why. There is no published example audit record, and no field list, retention period, export path, dashboard or query surface.

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Each ACT-2 run returns execution evidence. It returns results, screenshots, change records and artifacts for each request, so teams can audit what happened and connect execution back to the original decision. These four kinds of artifact come back with every run.

The record covers the agent's own execution, request by request, not the health of the customer's operations. Screenshots and change records show what the agent saw and what it changed. Because ACT-2 operates interfaces instead of APIs, there is no system log to inherit, so the artifacts come from Enhans's execution layer, not from the systems being operated.

Enhans presents this as a differentiator, setting its evidence and execution records against the limited verification of other tools. It describes enterprise automation as entering the required system, following the right process, completing the action and proving the result. It also says generic computer use does not scale, partly because teams need traceable records of what was executed.

Enhans gives no retention period or export path, no admin view across users or agents and no alerting, and does not say where these records are stored or for how long.

Sourceenhans.ai/act-2 execution evidence, comparison table and enterprise-execution framingread 2026-09-14

Data residency controls keep the customer's data where they need it, alongside role based access controls and audit trails. The platform also embeds into partner products, including a CMS, a customer support platform where it runs live through an Aigensei widget, and a behavioral health platform. There is no self hosted, private cloud, virtual private cloud or on premises option, and no region list. The customer controls where data resides but not where the software runs.

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AgentOS can be deployed on premises or in private cloud, matching customer infrastructure and security requirements. These are the customer's own environments, not regions Enhans runs, so the software runs where the customer is.

ACT-2, the part that touches the customer's systems, can also be deployed in controlled environments, including on premises or infrastructure the customer manages, where execution control and system access matter. Enhans sets this flexible deployment against hosted tools that execute in the cloud by default.

AgentOS builds a separate ontology lake while keeping existing systems in place. It stores the data it needs separately instead of changing enterprise databases, with no rip and replace. Because its data layer sits beside the customer's systems, it can sit inside the customer's estate.

For the default cloud there is no region list, residency commitment or data location statement. There is no installation or architecture documentation for an on premises deployment. Delivery is quotation based, so the boundary is negotiated for each customer instead of being specified up front.

Sourceenhans.ai/agent-os built for enterprise deployment section, enhans.ai/act-2 deployment comparisonread 2026-09-14

The Playbooks section offers six playbooks, each with its own page. They cover Sale Qualification, Customer Service Resolution, Onboarding and Intake, Operations and Approvals, HR and People Operations, and Finance and Compliance. Aigensei does not prescribe a workflow.

Customers configure their own playbook, and every workflow follows the customer's steps, logic and decision criteria instead of a generic template. The demo invitation asks prospects to share their highest priority playbook for Aigensei to walk through.

Aigensei does not say whether the six playbooks are starting configurations customers can adopt or worked examples of processes it configures.

Sourceaigensei.ai platform and home pages, site navigation and footerread 2026-08-31

A named set of prebuilt agents ships under CommerceOS. AgentOS itself is not a catalog of prebuilt agents. It is the platform where enterprises connect their data, define business meaning and build task specific agents. Enhans says prebuilt agents serve narrow predefined tasks while AgentOS gives enterprises the tools to build their own, and its four worked scenarios are representative scenarios, not fixed product packages.

CommerceOS offers seven specialized AI agents with their own pages and durable URLs. They are Price Agent, Promotion Agent, SNS Agent, QA Agent, Brand Protection Agent, Product Experience Agent and AD Agent. Each is a whole product that works without the others, and pricing, advertising, brand protection, social posting, quality assurance and review analysis are separate commercial functions.

Customers can take the agents one at a time. The market monitoring scenario uses Price Agent, Social Media Agent and Brand Protection Agent as its example agents, and industry pages pair single agents with single verticals, such as Ecommerce with QA Agent, Fashion with SNS Agent and F&B with Brand Protection Agent. AgentOS is sold as a build platform and CommerceOS as a packaged set.

There is no entitlement, install or enable action, and no pricing for each agent.

Sourceenhans.ai/agent-os not-a-catalog statement and use cases, enhans.ai/act-2 navigation CommerceOS specialized agent listread 2026-09-14

No model provider is named. There is no model picker, bring your own key arrangement, endpoint configuration, per workflow model setting or routing between models. The seven step architecture has a planning agent and a Writer Agent, and Aigensei does not say what models power them. Workflows can call external agents as tools, so customers can route work to agents they control. The model behind the platform's own agents is still not the customer's choice.

Sourceaigensei.ai platform and home pagesread 2026-08-31

ACT-2, Enhans's own computer use model, runs the actions, and the customer cannot choose or route models. There is no model selector, admin entitlement or model setting for an agent or workflow, no disclosed routing and no way to bring your own key or model. In Agent Builder a customer chooses agents, tools, rules and conditions, but not the model.

ACT-2 is the platform's headline technical asset, with its own product page, subdomain and benchmark claim. Enhans trained the action model itself and keeps the stack fixed, so the customer gets no choice.

Enhans names its own model but no provider, family or version for any other part of the platform. It does not say which models run the reasoning, planning and language layers across Agent Builder, App Builder and the ontology work, and it has no subprocessor list. A customer can learn what drives their screens but not what reads their ontology.

Enhans says ACT-2 ranks in the global top five on Online-Mind2Web alongside Google, OpenAI and Anthropic. Those three labs are benchmark peers, not models a customer can use on the platform.

Sourceenhans.ai/act-2 in-house model and benchmark claim, enhans.ai/agent-os Agent Builder configuration surfaceread 2026-09-14

An API trigger is one of four ways a request enters the execution architecture, so an outside system can start a process programmatically. There is no published endpoint, authentication method, API reference, SDK, developer documentation or developer portal. The MCP integration and external agent calls run the other way, with the platform calling out to live data sources. Partner products, including a CMS and a customer support platform, embed Aigensei under commercial arrangements, not through public developer access.

Sourceaigensei.ai platform and home pagesread 2026-08-31

AgentOS has no public API, SDK or developer surface. There is no developer section, API reference, documentation portal or docs subdomain, and no agent card, webhook or CLI. The builders are authoring surfaces inside the product.

The interfaces point outward. Pipeline Builder brings databases, APIs and files into AgentOS, consuming other systems' APIs, and ACT-2 drives external systems the same way. Neither lets an outside caller drive AgentOS.

Enhans mentions MCP based integration support alongside its deployment options but does not say which way it runs. An MCP server would expose AgentOS to a customer's assistants, while an MCP client would only consume tool servers. The offering is quotation based.

Sourceenhans.ai/agent-os and /act-2 navigation and Pipeline Builder, Microsoft Marketplace listing unreadread 2026-09-14

Customers configure a playbook and the platform executes it. There is no preview, sandbox, simulation, test run, scoring, test set, expected outputs, version comparison or debugging view. Through continuous improvement the platform learns from outcomes over time and gets better as it operates without manual retraining. That loop is internal, and customers get no result they can read or compare. The full audit trail records what happened without scoring correctness, replaying cases or comparing configurations.

Sourceaigensei.ai platform and home pagesread 2026-08-31

Customers have no way to test or evaluate the agents. There is no evaluation harness, scored test cases or golden set, no judge, regression suite or A/B comparison between agent configurations, and no release quality gate or loop that measures the deployed agent against outcomes. There is no debugging surface either.

Agents built on the ontology automate decision domains including cost analysis, audit and simulation. That simulation models the customer's business, such as a pricing scenario or a cost structure, not whether the agent's work was correct.

Enhans says ACT-2 ranks in the global top five on the Online-Mind2Web computer use benchmark, alongside Google, OpenAI and Anthropic. The benchmark measures the underlying action model, not agent behavior on a customer's work, and customers cannot run it against their own workflows.

ACT-2's execution evidence and its checks and confirmations in processes with several steps show what happened on a given run. They do not show whether a change to an agent made it better.

Sourceenhans.ai/act-2 execution evidence and comparison table, enhans.ai/agent-os use casesread 2026-09-14

Workflows call RAG knowledge bases, Model Context Protocol servers connected to live systems and external agents, all of them programmatic interfaces. The platform connects to any live data source through MCP. There is no browser control, navigation, form filling, screen interaction, scraping, visual grounding, desktop automation, code execution or web access tool. The widget in a partner's customer support platform places Aigensei inside another product's interface, and Aigensei does not operate that interface.

Sourceaigensei.ai platform and home pagesread 2026-08-31

ACT-2 is the enterprise computer use layer for AgentOS, covering the browser and desktop applications. It is the AI execution layer that turns decisions into actions across browsers, business systems and operational tools, connecting to browsers, SaaS platforms, admin panels, internal tools and operational portals. It operates browsers and business tools through a governed execution layer.

The agent navigates and performs the workflow in a controlled computer use environment that Enhans runs. ACT-2 is Enhans's own model, not another company's engine, and Enhans contrasts it with hosted CUAs built for help with individual tasks.

Business tools change screens, fields and workflows over time. When a screen, field or workflow changes, ACT-2 can support fallback execution and workflow updates. Enhans built it for limited API coverage, working across tools and portals where APIs are incomplete, unavailable or impractical, since many enterprise systems do not expose complete APIs.

The execution cycle has four steps. In Explore, ACT-2 maps the target system and identifies the actions needed. Execute then runs the workflow in the controlled environment, followed by Reuse and Adapt.

Sourceenhans.ai/act-2 computer use layer, core capabilities and execution cycleread 2026-09-14