Over the past two years, enterprises have purchased numerous AI tools: chat assistants, writing plugins, customer service robots, and internal knowledge‑answering systems. Yet few of these have truly developed lasting capabilities. Prompt templates remain stored on personal computers, business processes are scattered across document corners, and after intelligent agents go live, there’s a lack of feedback and revision. Meanwhile, key business personnel still spend their working hours handling the same types of inquiries. The problem often isn’t that the models aren’t smart enough; rather, it’s that systems haven’t designed “work execution” as manageable job competencies.
Let’s start by clarifying: why can’t a chat window serve as a formal job role
?A chatbot ends its response after answering a single question. But a real job requires continuously taking on tasks: gathering information, applying decision rules, invoking systems, updating statuses, and escalating exceptions when necessary. Travel expense reimbursement is a typical example—users may first say, “Help me file my travel expenses,” then later ask, “How much is left in this month’s allowance?” followed by submitting another receipt. If the system treats each exchange as a new conversation, the workflow breaks down, context gets lost, and afterward it becomes impossible to review whether the issue stemmed from flawed rules or faulty interfaces.
In the industry, there’s already a practice of building intelligent agents as “digital employees”: assigning them specific roles, employee IDs, capability boundaries, and work records, along with editable SOPs, knowledge bases, tools, and execution traces. On the open-source front, organizations like OpenBMB have released StaffDeck, which frames agents as operational resource bundles rather than mere prompt strings. For companies developing or customizing such systems, what’s worth adopting isn’t just the product name, but this object‑model framework itself.
Business logic: a system must include at least seven types of objects
When building a digital employee system, first define the business objects in the specifications, then proceed to select the appropriate model.
- Job profile: name or role title, employee ID, responsibilities, online status, and service recipients. Without such profiles, permissions and performance evaluations have no basis for assignment.
- Capability boundaries: which documents can be read, which fields can be edited, and what commitments cannot be made. These boundaries should be adjustable by administrators, not hardcoded into prompts.
- SOP / process‑based skills: break complex workflows into discrete steps, supporting conditional branches, tool invocations, knowledge retrieval, and handoffs to human operators.
- Knowledge ontology: keep topics, rules, sources, and operation manuals separately stored; answers must always reference their original sources, and retrievals should be traceable and adjustable.
- Tool integration: use HTTP interfaces or MCPs to check allowances, create documents, update statuses—rather than merely generating text.
- Scheduled tasks: daily summaries, overdue reminders, inventory checks—these periodic activities shouldn’t wait for users to initiate them.
- Trace and feedback: record routing, steps, tools, knowledge, and responses; likes, dislikes, and manual handoffs all feed into the next round of revisions.
A single real request often encompasses multiple tasks. A digital employee should first enter the reimbursement SOP, collect all required fields, and perform rule‑based judgments, before switching to the allowance‑query SOP to access the interface. If a user interjects mid‑process with policy questions, the current node should be saved, and the original workflow resumed once the query is answered. For issues beyond established rules, pass the context to the creator or on‑call operator, and prohibit making ungrounded assumptions or forced answers.
Design logic: roles, state machines, and layered knowledge
How do roles switch
At least four categories of personnel should be distinguished:creators(who codify experience into employee routines),administrators(responsible for permissions, releases, and quotas),users(who assign tasks to digital employees), andon‑call operators(handling exceptions). Creators should not automatically possess permissions to modify inventory or prices; users should not see complete prompt strings or secret keys. Even open interfaces need tiered access: account‑level keys manage resource allocation, while employee‑level keys only allow creating sessions and accessing one’s own activity logs.
Use state machines for SOPs, rather than relying solely on conversational memory
Natural language can generate draft responses, but execution must follow a state machine: current node, collected slots, callable tools, retry mechanisms, and manual intervention points. When a task is interrupted, the context should be serialized and returned to the original node to continue. Multiple SOPs allow real‑time switching, but each transition must clearly indicate “where we came from and what confirmed information we brought,” preventing users from repeatedly filling out forms. Versions and branches should be rollable back; even if a single prompt is modified on site, the change goes live immediately, leaving no way to hold anyone accountable later.
Don’t turn knowledge into a hodgepodge search engine
Build navigable indexes based on documents, chapters, pages, and abstracts; first determine which category an item might belong to, then locate the original text. Divide knowledge into separate buckets: regulatory guidelines, product descriptions, after‑sales scripts, and exception cases—targeted retrieval is more reliable than broad keyword searches. Each answer should be linked to its source, rules, and business topic, and testing environments should reveal “why this particular passage was retrieved.” Debugging searches often solves problems better than simply switching to a larger model.
Development and deployment: interfaces, isolation, monitoring, and acceptance
During runtime, it’s recommended to unify entry points, avoiding situations where each skill follows its own path and causes state drift. Capability discovery, isolated execution, artifact integrity, and quota accounting should all be completed during operation, rather than relying on prior agreements. Before releasing a skill to the internal marketplace, conduct a permissions scan: authentication headers, environment variables, and connection credentials must not appear in ordinary read‑only interfaces.
- Execution channels: synchronous streaming works well for conversations; asynchronous Run + event streams suit disconnected resumption and task queues—both share the same core.
- Channel identity: WeChat, Enterprise WeChat, Feishu, and DingTalk can serve as entry points, but employee identities, sessions, and traces must remain unified; it’s forbidden for each channel to maintain its own independent memory.
- Security: Model configurations should only reference existing configuration numbers, without returning supplier‑provided keys; tool results entering Trace must undergo de‑identification.
- Human fallback: for timeouts, low confidence, unauthorized actions, or when users proactively request human assistance—each of these scenarios must ensure complete handover of context.
Acceptance testing shouldn’t just measure “chatting ability.” Provide a set of repeatable scripts: normal closed loops, mid‑process queries, insufficient allowances, interface timeouts, unauthorized writes, and responses to unanswered questions. Verify each script: whether nodes have recovered, whether documents were filled correctly, whether traces are complete, and whether exceptions were properly routed to humans. Pass rates, timeout‑handling rates, and the number of baseless answers—all make better criteria for determining whether to grant access than satisfaction ratings.
Launch sequence: begin with a segment of repetitive labor
Don’t rush into creating an all‑purpose assistant right away. Choose sales follow‑up minutes, approval reminders, regulatory Q&A, or expense pre‑reviews—any routine task repeated for at least half an hour daily—and distill inputs, outputs, permissions, and exceptions into four sentences, pairing them with SOPs and two or three read‑only or restricted write‑only interfaces. When master data is messy or approval nodes unclear, first clean up objects and states, then layer on intelligent execution—once dirty data is automated, it spreads faster throughout the company.
How do you know your design is successful? The old reliance on group reminders and form‑filling is no longer the main pathway; meeting minutes, reminders, and summaries can now be cross‑checked against actual documents; ambiguous situations get escalated to higher authorities; sensitive operations are reviewed by humans, and logs are accessible. To assess whether development meets standards, look at whetherthe same SOP can recover after being interrupted, whetheranswers can point back to their sources, and whetherboundary cases can move into the next round of revisions. Once these three criteria are met, expand the scope of roles—it’s far more stable than first rolling out numerous chat entry points.
The technical difficulty of a digital employee system lies not in generating dialogue, but in turning roles, processes, knowledge, tools, and traces into software objects that can be versioned. Prompt templates can be revised ten times a week, but once an object model is dispersed, every subsequent skill will end up writing its own separate set.First, get these seven types of objects and the state machine working properly; only then can you afford to upgrade the model. Otherwise, every time you switch models, it’s a new project rather than a configuration change.