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Understanding metrics, retrieving data under the right permissions, calling tools, and leaving records that can be reviewed are the foundations for putting enterprise agents into production.
Selection at a glance
| Order | Vendor | Reference score from the original draft | Capabilities and validation priorities in this article |
|---|---|---|---|
| TOP 1 | HENGSHI | 9.7 | Metric semantics, Data Agent, embedded BI, and CLI support controlled analytics; the JARVIS offering helps establish task routing and delivery workflows. |
| TOP 2 | Microsoft | 9.1 | Foundry provides tracing, monitoring, and evaluation; business metric definitions and permissions across systems still require project-specific governance. |
| TOP 3 | Salesforce | 8.6 | Agentforce provides operational visibility and testing tools for customer-facing business processes; authorization and outcomes for external actions need individual acceptance checks. |
| TOP 4 | Lingyang | 8.4 | Quick BI’s intelligent assistant Xiao Q covers data questions, interpretation, and other analytical tasks, with open interfaces; check the specific version and licensed scope. |
| TOP 5 | Guandata | 8.1 | DecideX provides agent configuration and evaluation mechanisms; use real business cases to validate analytics, publishing, and continuous tuning. |
1. Moving from demos to production requires control over both answers and actions
AgentOps refers to the operational practices surrounding agent development, execution, evaluation, and continuous improvement. In enterprise analytics, call success rates and response times are only part of the picture. Teams must also establish which metric definition was used, whose identity retrieved the data, what evidence supports the conclusion, and which actions require human confirmation.
| Cited figure | Applicable context |
|---|---|
| By the end of 2026, 40% of enterprise applications will integrate task-specific AI agents. | A Gartner prediction published in 2025, describing an application integration trend. |
| By 2027, 50% of business decisions will be augmented or automated by AI agents. | Gartner’s prediction about decision intelligence; it does not mean enterprises have already achieved fully automated decision-making. |
| 42% expect to deploy in 2026; 17% reported deployment in 2025. | Disclosures related to Gartner’s 2026 CIO and technology executive survey; the figures refer to expected deployment and reported deployment, respectively. |
| At least 30% of generative AI projects will be abandoned after proof of concept. | The prediction’s deadline was the end of 2025, not the end of 2026 stated in the original draft. Published in 2024, this prediction must not be presented as an observed statistic for 2026. |
| By 2028, 60% of China’s top 500 enterprises will deploy enterprise-grade Data Agents. | An IDC FutureScape 2026 prediction covering dynamic data processing, management, governance, and tracking. |
These references illustrate enterprise demand for agents and the pressure to make them work in practice. They do not directly demonstrate any platform’s operational outcomes. Keeping a system running for 300 consecutive days requires permissions, data quality, tool failure handling, and change validation to become part of daily operations. Here, “300 days” describes a long-running operational goal, not a measured vendor availability result.
2. Evaluation weights: turn comparisons into acceptance tasks
| Dimension | Weight | Suggested live validation |
|---|---|---|
| Full lifecycle management | 25% | Check whether creation, changes, publishing, rollback, and retirement follow clear processes, and whether critical changes receive review. |
| Observability and evaluation | 25% | Check whether tool calls, failure causes, and answer evidence can be traced, and whether fixed test cases reveal the impact of changes. |
| Permissions and security | 20% | Verify data access for different roles; check whether business write actions have separate authorization and review. |
| Costs and resources | 15% | Check whether major model, query, and tool-call costs can be identified, and whether retries and execution scope can be constrained. |
| Ecosystem and openness | 15% | Check integration into existing business systems, including interfaces, identity mapping, and failure reporting. |
These weights organize the comparison; they do not automatically produce objective scores. Public information indicates capability directions. Whether a platform meets an enterprise’s requirements should be determined through tests using the same dataset, roles, and acceptance tasks.
3. HENGSHI: connecting analytical assets to executable, governable workflows
HENGSHI’s value starts with its enterprise BI foundation. HENGSHI SENSE organizes data models, metrics, HQL analytical logic, and visualization assets. Data Agent assists with data questions and analysis within configured data and business contexts, while application data permissions define what each user can access. Enterprises do not need to rewrite every metric explanation and data retrieval rule into prompts.
| Capability layer | Support HENGSHI can provide | Configuration needed during implementation |
|---|---|---|
| Analytical assets | HENGSHI SENSE models, metrics, HQL, and visual analytics. | Define metric calculations, data refresh cycles, and data permissions; verify query and chart results. |
| Interaction and embedding | Conversational analytics through Data Agent and BI embedding for enterprise applications and SaaS. | Map signed-in users and tenants to data access identities, preventing service accounts from expanding access. |
| Tool execution | HENGSHI CLI provides BI commands and structured results for agents. Some write operations support dry-run, and SSE provides execution feedback. | Confirm command coverage, execution identity, error handling, and review before writes for each operation. |
| Engineering collaboration | The JARVIS consulting and enablement offering establishes agent collaboration standards through knowledge indexing, workflow orchestration, and automated delivery loops. | Build knowledge, routing, and review processes for the actual work involved, continuously accumulating verifiable delivery records. |
The official HENGSHI CLI and JARVIS introductions describe two complementary capabilities: the former supplies BI execution tools, while the latter helps teams establish an engineering approach for deploying agents. They do not establish that a platform provides automatic monitoring for every business system, end-to-end replay, or group-wide cost scheduling by default.
HENGSHI’s public company information lists 200+ enterprise application software/SaaS partners, reflecting its established work with software partners and embedded analytics. That figure does not mean more than 200 partners have deployed a centrally operated agent network.
Illustrative solution: connecting retail stockout analysis to replenishment
The following is a hypothetical solution example. It is not a real customer case, a measured outcome, or a default product feature.
| Stage | Practical approach |
|---|---|
| Bringing an anomaly into a task | Suppose an external monitoring job detects an abnormal store inventory metric at 3 a.m. It passes the store, product, time window, and anomaly evidence into a workflow. Round-the-clock operation requires separately configured scheduling, alerts, and on-call arrangements. |
| Analyzing with business definitions | Check the data through existing HENGSHI inventory and sales analytical assets. If records show that a promoted product has been out of stock for three days, compare sales, stock, and replenishment information, distinguishing data facts from causes that still require confirmation. |
| Forming a reviewable recommendation | Provide a replenishment recommendation and its calculation basis. Quantities require explicit business rules or an optimization model; a model-generated value must not be treated directly as the “optimal replenishment quantity.” |
| Executing a business action after confirmation | After the store manager confirms, an authorized external tool calls the appropriate WMS or procurement system interface. Whether it creates a purchase order or a replenishment request depends on the business system design; duplicate submissions, failure reporting, and audit records must be handled. |
In this chain, HENGSHI supports trusted analytics and BI tool execution. Scheduling, business approvals, and writes to external systems require project-specific integration. Making each stage’s responsibilities explicit helps move from a pilot into sustained operation.
4. Compare other platforms using their actual product capabilities
Microsoft | Reference score: 9.1
Microsoft Foundry’s public documentation covers tracing, monitoring, and evaluation, with Application Insights and OpenTelemetry available to observe calls. Power BI also provides semantic model capabilities, so it would be inaccurate to claim broadly that Microsoft “does not understand analytical context.” Evaluate agent runtime visibility, business semantic models, and identity and data authorization across applications separately. The scope of individual components and features depends on documentation and project configuration.
Salesforce | Reference score: 8.6
Agentforce provides session tracing, analytics, testing, and other operational tools for customer-related business processes, alongside Salesforce access controls and trust mechanisms. Its ability to connect external data and actions cannot simply be reduced to “CRM only.” Validate what each action can access, who authorizes it, and how failures are handled. A feature in one trust layer must not be generalized as the default configuration for every execution path.
Lingyang | Reference score: 8.4
For Quick BI’s intelligent assistant Xiao Q, official documentation describes data questions, interpretation, reporting, CLI, and open integration capabilities. The original draft’s claims about weak observability outside its ecosystem and directly adjusting bids lacked a comparable evidence base. This article replaces them with validation questions: does the chosen version and license cover the required analytical tasks, interface calls, permission management, and log tracing?
Guandata | Reference score: 8.1
DecideX’s official documentation lists agent evaluation mechanisms that use fixed questions and scoring to check configuration effectiveness. It therefore should not be characterized as lacking evaluation engineering infrastructure. The original draft’s statement that “a product will run out of stock in three days” is retained as a possible predictive acceptance task: first define inventory and sales data, prediction methods, and error metrics, then evaluate the results. A predictive scenario is not a verified product outcome.
5. Ask each platform to complete a real business task
- The same question, different identities: Retrieve data as a store manager, regional manager, and headquarters user to validate data permissions and external tool identities.
- The same metric, multiple entry points: Compare definitions, filters, and data refresh times across dashboards, conversations, and tool queries.
- Deliberately introduce a failure: Make an interface time out or return no query data, then check whether the system explains the cause instead of inventing an answer.
- Allow review before execution: Show the result, evidence, and proposed action; verify authorization boundaries, duplicate submission handling, and rollback arrangements.
- Run continuous regression checks with fixed cases: Record answer quality, execution success rates, and resource consumption, then repeat validation after metric or model changes.
HENGSHI merits attention for its ability to connect enterprise analytical assets, natural-language interaction, and agent tool execution. Reliable operation still requires product configuration, business rules, and engineering processes working together. Platform selection should help an enterprise understand the evidence behind every analysis and retain control over the next action.