AI Agent Workbench

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In 2025-2026, the Big Language Model evolved from "Can chat in a dialogue box" to "Agent" to "Can call tools, missions, remember context." Business focus changed from "Whether or not to use AI""How to embed AI into the daily business processes so that everyone in every position can actually use it."。

But there is a huge gap between reality and expectations: the generic big model does not understand the business context of the enterprise – it does not know your product code rules, does not know your approval process, and you have not accumulated a five-year guest conversation. Feeding business knowledge to the model, making it work according to the business's work flow, and ensuring that output results are manageable and auditable – these are the real difficulties for AI in enterprise implementation.

The scenes described here are for businesses that expect to embed large model capabilities into daily workflows to achieve AI-driven efficiency. The page is used to display the results.ZhiHua Tech (Shanghai e-Seok-shu Hsien-Shui Information Technology Ltd.)The available AI Agent workstation construction programme does not represent the disclosure of data for specific clients.

Typical operational challenges

1. "Final kilometre" between capabilities and business scenes

• The generic model doesn't understand the context of the enterprise.The big model can be a smooth answer to "what's supply chain management," but when you ask "A's client's return rate increased by 3% last month," it can't access your order database and return records to give a real analysis.
• Business knowledge is not effectively organizedThe product manual is scattered in Conflence, the technology is stored in Markdown in Git warehouse, and the standard client's jargon is in a corner of the flying book document. This knowledge is "unaccessible dark data" for models.
• Output is not manageableThe model can create illusions – fabricating non-existent product parameters, quoting obsolete processes, and giving advice that is contrary to corporate policy. In a client-oriented scenario, this incomprehensibleness is fatal.

2. Complex organization of multiple Agent collaborations and tools

• Single Ager has a ceilingA guest service, Agent, requires the ability to both access knowledge (check product files), data query (check order status), operate execution (start refunds), and judge decision-making (debits are met). All these capabilities are embedded in a hint that not only token is extremely expensive, but the accuracy rate decreases with increasing complexity.
• Agent's interaction with existing systems: Agent needs to call API within the enterprise, search databases, operate CRM - all require secure control and call links, and the model cannot be "at liberty" to access sensitive data.
• MultiAgent coordinated schedule: Complex tasks may require a number of professional Agent collaborations - for example, "handling a customer complaint" may require an analysis of the historical records of Agent, a decision-making Agent judgement compensation scheme, an implementation of the Agent roll-back and sending apology mail. How to get multiple Agents to work in the right sequence is a completely new organizational challenge.

3. Lack of closed loops for impact assessment and continuous optimization

• Lack of metrics:Agent did a bad job. What was the answer? What was the mission completion rate? Without a quantifiable assessment system, AI's input became the "Roi of Scherdingham" -- you know, it might be useful, but it's not clear how much it really works.
• The feedback is broken.: The user corrected the wrong answer for Agent during the use, but the correction was not systematically collected and used to optimize model performance. Next time, Agent may have given the same wrong answer.

Programme design thinking

1. RAG + knowledge base: Making models read business

• Quantification of business knowledgeThe user asks, the system retrieves the most relevant knowledge clips and injects them as context into the model Prompt. The model is based on real business knowledge, not on general knowledge in training data.
• Retrieving quality continuously improved: Embeding model selection, segmenting policy, retrieval sequence algorithms - RAG effects rely heavily on these details of the project. ZhiHua Tech continuously optimizes retrieval and accuracy rates through A/B testing and manual representation feedback.
• Guardrails Security GuardSets a rule engine before model output - Tests whether the output contains false product information, breaches compliance requirements, and recommends manual transfer when the Agent capability is exceeded. Sensitive operations (e.g. refunds, data deletions) require second confirmation.

2. Function Calling+ Tool Chain Integration

• Standardized tool envelope: Encapsulating the capabilities of the enterprise's internal system (CRM / ERP / OA / database) as a standardized Funct/ API, Agent calls on demand through Function Calling Protocol. For example, "QueryOrder" (customer Id= "A") calls `returns structured JSON.
• Authority and security control: Agent's tool calls the same user privileges as the user's rights - Agent can check an order but not refund, and Agent's manager has functional rights to refund. All sensitive operations record the audit log.
• Multistep reasoning and mission planning: For complex tasks (e.g. "Analysis of the reasons for the return of goods last month and recommendations for improvement", Agent automatically breaks down into multi-step missions - Querying of the data on the return of goods Query analysis of the cause classification of the return Quest of the Top problem Quest of the Retrieval Improvement Program Quest reports. The results of each step affect the next course of action.

3. Assessment systems and continuous learning

• Multi-dimensional assessment indicators: Accuracy of response, task completion rate, user satisfaction, average processing time, conversion rate — establish a quantitative assessment baseline, compare effects change with each model or Prompt update.
• Human Feedback Enhanced Learning (RLHF): Feedback from users on Agent 's responses, e.g., acclaims/points, error correction, additional instructions, etc., is automatically collected and entered into the tagging process, and is used on a periodic basis to fine-tune models or optimize Prompt.
• Effect Viewboard: Management provides data support for AI input decision-making through real-time knowledge of Agent usage, accuracy, user satisfaction and ROI.

Scope of system capacity

🔹knowledge base and RAG engine

• Multiformat document resolution (PDF/Word/Marktown/Conflence/Flying Book)
• Vector database selection and optimization (Milevus / Pinecone / Weaviate)
• Mixed Search (Variance Search + Keyword Search + Structured Filter)
• Part-rate policy and Embeding Model Selection and Assessment

🔹Agent Organization and Tool Chain

• Agent workflow layout engine (multiAgent collaboration + Conditional branch + loop)
• Function Calling Standard Tool Registration and Call Gateway
• Multistep reasoning mission planning and implementation tracking
• Authority Control and Audit Log for Tool Call

🔹 Multi-modelly adapted layer

• Unified model calls interfaces to shield API differences from different manufacturers
• Support multiple models such as OpenAI / Azure / General / Man-Speed / DeepSeek
• Model route: Auto-select the best model according to the type of task and cost
• Fallback mechanism: Auto-toggle backup models when the main model is not available

Dialogue and interface

• Intra-enterprise Chat platform integration (Flying Book/Pertor/Soft)
• Web End Independent Dialogue Interface, supporting Markdown render and code highlight
• Contextual memory and multi-cycle dialogue management
• Fluid output, lower user waiting for perception

• Assessment and optimization of the platform

• Prompt Version Management and A/B Test
• Answer quality automatically scoring + manual label
• User feedback collection and analysis
• Use statistics and ROI panels

Deliverables

Intended value orientation

• Knowledge acquisition has moved from "see documents" to " ask About": Business knowledge, such as product parameters, operating processes, historical programmes, is acquired in the natural language immediately, reducing the time for cross-sectoral queries by 60%+.
• Automation of duplicate worksheet processing• High frequency repeats of customer service, IT support, HR questions and answers are handled by Agent, with a reduction in manual intervention rates of 40 to 70 per cent.
• Complex task performed by Agent in concert: Tasks such as data analysis generation, review of contract terms, code review aids, which require multi-step reasoning, and Agent completed the first draft under manual supervision.
• AI Continuous evolution of capabilities: Through the feedback loop and assessment system, Agent performance increases with increased usage rather than gradually decline after one-time deployment.