How to Create an AI Worker: A Complete Guide to Virtual AI Agents for Task Automation

We have recently launched a research project focusing on developing ways to engage with payroll employee digital users via technology testing on 43 technologies related to how technological solutions can be tailored to hub-based or centralized functions. We feel we have identified an excellent approach to corporate work environments to explore and engage employees who will be using payroll technology to support business processes. The key findings to date show that as long as we have clarity on the task, we should be able to develop a solution that automates that task. Once we have developed a solution, the next step is to design that solution using one or two of the available options.

Introduction to the AI Employee

An AI Employee is defined as an autonomous digital agent or automated business agents created by Large Language Models (LLM) which allow the agent(s) to perform business functions without having permanent oversight by an employee. In this scenario, the agent processes original customer requests, processes and resolves those requests in a timely manner, performs as if it was an actual employee, identifies and analyses data to build reports, and works with corporate data using APIs. The primary distinction between AI agents and an average chatbot (due to their fixed decision-making tree), is that AI agents use the context of the current situation to determine how to make decisions on the fly and develop their own methods of performing their tasks.

AI Employee Fundamental Attributes:

• Autonomy: AI's autonomy is defined as having the ability to launch automatically based on a trigger (time, event, message), is able to perform necessary actions without intermediate supervision, determines the method of completing those actions, and does so in a timely manner.
• Contextual Understanding: AI can read and interpret Natural Language Processing (NLP), understand customer intent (i.e. "I want to buy," "I have a problem with my order"), identify the key elements (i.e. name, date, amount), and provide a series of responses to customers.
• Integration with Company Systems: AI can retrieve and store information in a CRM, ERP, and/or spreadsheet. AI can use Retrieval-Augmented Generation (RAG) to improve accuracy against the corporate knowledgebase.
• Scalability: Single agent instances can accommodate 500 to 5,000 requests per hour based on complexity. Requests cost $0.001–$0.050, while Human First-line Operator costs $2.00–$5.00.

In 2027, 40% of business processes for knowledge workers will be automated via AI agents freeing 2.3 billion human hours annually in the US alone.

Differences Between an AI Agent, Standard Software, and Robots

Hardware Differentiation: Traditional Software runs on a pre-defined algorithm, e.g. A pressed, B happens. RPA (Robotic Process Automation) Robots can imitate the actions of humans in a user interface; however, they will not function under a dynamic operating script, i.e. if you change one item on a page, they stop functioning.

AI Agents operate differently:

• Logical Adaptivity: Classic IF-THEN scripts manage a specific number of variations (3–5). AI Agents powered by LLMs can identify thousands of like-analogous requests (for example, "Return my money" or "Cancel payment") and choose the appropriate response/action.
• Working with Unstructured Data: RPA reads tables via individual cell coordinates versus AI Agents are capable of determining the meaning of email, PDF invoices and voice messages and converting that into structurally-formatted items (e.g., JSON i.e., an object that has fields for amount, date and counterparty).
• Low Cost of Changing: When adding a feature to a conventional software application, it generally takes from 2 to 6 weeks to do so because it requires launching a new software sprint. However, in the case of an LLM agent, the feature can often be added by rewording an input prompt or by adding a new API node.
• Exceptions Management: When an RPA program encounters an error, it usually just stops execution. By contrast, when an LLM agent encounters an error, it either tries to reason its way to the correct output using a chain-of-thought prompting (aka either a reasoning step) or it submits a detailed description of the problem to a human moderator (operator).

Example: We have a client that utilizes RPA to capture information from users in their Telegram application and populate that data into a Google Sheet. When the user submits the input "call me" instead of a phone number, the robot generating this input does not know how to handle this exception, so it stops the execution of its RPA process. By contrast, when an ASCN.AI agent receives the same input, it recognizes the intent behind the user input and prompts the user for clarification. Once it has received the phone number from the user, it continues executing the RPA process as if there had been no exception and as if the input had originally been a valid input.

Key Technologies for Building a Virtual Employee

To build a virtual employee, an AI agent is constructed with an LLM as its base technology—an LLM is constructed using a neural network model that has been trained on petabytes of text data in order to predict what the next token is likely to be. This means that although GPT-4o, Claude Sonnet 4.6, and Llama 3.3 are text generators, they are also reasoning tools; they can deconstruct a complex request into a series of simple sequential requests, call third-party APIs to fulfil each of the requests and combine the results produced by each of the requests into the final answer to the initial complex request.

The main machine learning technologies that can be used to develop AI employees include:

• Transformer Networks: A self-attention-based network architecture that dynamically considers all of the prior context of the entire conversation (up to 128,000 tokens of context for a single use of GPT-4 Turbo) versus the old Recurrent Neural Network model that only considered a small number (typically from 5 to 20) of prior context tokens.
• Fine-tuning with corporate data: When an LLM is fine-tuned using a limited amount of a corporation's proprietary data, the overall accuracy of all worker responses is increased significantly. Fine-tuning a model with 500–5,000 niche-specific queries can bring the accuracy level up to nearly 97% in terms of the F1 metric.
• Retrieval-Augmented Generation (RAG): The agent searches for relevant snippets from vector-based data stores (like Pinecone or Qdrant), thus reducing the hallucinations of fact-critical tasks to only 2–5%.
• Function Calling: The model not only describes the action to be taken (such as sending an email, updating a CRM, checking stock), but it actually executes the action through an external API.
• Chain-of-Thought Prompting: A technique that allows the model to "think through" the answer prior to providing a response has been shown to increase the performance of multi-step tasks from 34% to 78%.

As an example, at ASCN.AI we use a hybrid AI approach of RAG + fine-tuning for crypto analytics: the agent collects data from on-chain metrics, and the fine-tuned model performs specialized analysis for DeFi. Compared to the non-fine-tuned GPT-4 model, we have achieved a 23% increase in volatility forecasting accuracy.

NLP Technologies and How GPT is Contributing to the Evolution of AI Agents

Natural Language Processing is made up of several different technologies that allow machines to represent human language in a textual format, and then back to human language again. While the first stage of the GPT Era lasted from 2018 until 2020, and included a number of independent NLP Models (one for intent classification, one for entity extraction, and one for text generation) that each required training on tens of thousands of examples, the release of both GPT-3 and GPT-4 changed the landscape of NLP. With both GPT-3 (released in 2020) and especially GPT-4 (released in 2023), there is now a single, universal model that can perform all NLP tasks simply through prompt engineering, without needing any additional training. Examples of the current capabilities of LLMs relate to Natural Language Processing (NLP):

1. Identifying user intent with 98% accuracy regardless of the way someone phrases their intent (intent classification).
2. Extracting data from text to become a structured piece of information (e.g., “Call me back tomorrow at 15:00 at +79001234567” will allow the user to be notified).
3. Knowing whether tone is positive, negative, or neutral to prioritize appropriately (sentiment analysis).
4. Creating brief summaries of lengthy communications/documents to provide efficiencies for managers (summarization).
5. Allowing the AI employee to communicate effortlessly in the customer's language (50+ languages supported).

While GPT cannot be fully credited for progressing through the evolution of AI agents, its rapid evolution has made it possible to create a bot in hours with no coding using the ASCN.AI development platform. Previously development of an AI agent typically required many months and a workforce of NLP engineers and linguists.

Platforms and Tools Facilitating AI Employee Development

The AI agent development platform determines the speed of initiation, flexibility of logic, and overall cost of ownership of the resulting AI agent's environment. Within this arena, AI employee developer tools can be categorized into three types of toolsets: no-code builders, low-code frameworks, and code-first solutions. Here are some details about the four main platforms for Creating Digital Industrial Agents:

So, why did we choose to build our Digital Industrial Agent (DIA) using ASCN.AI?

• ASCN.AI has been built to provide full end-to-end support for all financial instrument types.
• We can easily integrate our existing service providers into our deployment solution for our DIA using ASCN.AI.
• ASCN.AI has native support for all major crypto protocols, including Ethereum and Solana, and has the ability to connect to all major crypto-node applications without requiring any additional code to be written.
• ASCN.AI has built-in, vectorized knowledge bases for storing documentation, historical tokens, and deal cases that you can query instantly using embedding algorithms to find specific data about each asset type.
• ASCN.AI has provided us with a secure method of storing our API keys and tokens by encrypting them so that we can safely share them with other users.
• We can create full working AI agents with ASCN.AI that contain self-correcting, cyclic reasoning, and can call on their creator’s tools to resolve issues encountered throughout the creation process.
• ASCN.AI has developed a white-labeling option that enables very large organizations to create a customized branding experience.

Example of a Successful Automated Trading Platform: The crypto market crashed 18% in 4 minutes on Friday, October 11, 2024, resulting in an agent's data collection from 12 major US exchanges, analyzing the on-chain liquidations used to trigger a short signal in Telegram for the benefit of $847 before the crash began to recover; this would not be possible through manual trading due to the 15–20 minutes required to perform similar transactions.

Steps and Algorithm for Creating an AI Employee

The biggest mistake is to attempt to automate everything without first determining which day-to-day functions can be automated and/or made more efficient; routine activities are generally those that account for 40–60% of the overall time spent working, have little to no emotion involved in performing them, and do not require any creativity or imagination.

Criteria for Identifying When to Automate Tasks:

1. Task has been performed repeatedly 10+ times per day following the same procedure;
2. Established rules/guidelines (SOP/MOP/etc.) exist concerning how to perform the task;
3. Input data is standardized (e.g., CRM cards/forms/Telegram messages);
4. Completed tasks can be measured objectively; i.e., speed, accuracy, and error rate;
5. Few consequences for errors made; can be corrected easily. Example: Confirming an appointment vs. confirming a high value financial transaction.

Daily routine activities breakdown by department:

• Sales: Sales leads qualification, sending out commercial proposals, sending reminders to customers, creating summaries of CRM calls.
• Support: Answering frequently asked questions, receives escalated requests from clients, receiving feedback.
• Human Resources: Screening resumes, scheduling interviews, onboarding new employees.
• Accounting: Reconciling payments, sending reminders on accounts receivable, providing reports.
• Marketing: Parsing mentions of our brand/media outlets, generate SEO texts, conducting A/B testing.

Methods of Assignment: Complete table 3 axes from 1 to 10 (high frequency-low time, high frequency-high time, low frequency-low time) using results from axis 1 multiplied with axis 2 and divided by axis 3. Automate high-scoring tasks.

With a focus on real-world examples: An arbitrage trader spent 4 hours a day checking spread prices manually on exchanges. Building an agent to check 18 pairs of prices across 6 exchanges every 10 seconds, the agent generates a Telegram alert should the price spread move beyond 2%. With the time saved (28 hours a week), they were able to realise a 34% growth in revenue during a single quarter.

Designing Interaction Scenarios

A use case describes the anticipated behaviour of an AI agent in response to an event, including the following: trigger condition(s), execution steps, exception handling requirements, and metrics for evaluating success.

Example: Consultation request processed via Telegram

• Trigger: User receives a message from a user stating that "I would like a consultation."
• Context: Check user status and history.
• Scenario: The agent will communicate to the user times available for consultation, extract the date/time from the response, create a Google Calendar event for the consultation, and provide confirmation to the user, adding the lead to a CRM system.
• Alternative Scenario: Timely responses will be provided to the client with alternative times for consultations, and they will be notified if they did not provide a timeframe or if type of error occurs when using the API.
• Completion: Google Calendar event will be created, user notified of event, data will be added to the CRM system.
• Metrics: Time taken < 90 seconds, correctness of information provided >85%; NPS of agent = >8.

It is possible to build an interaction scenario use case by using blocks in ASCN.AI for Telegram, AI Agent, Google Calendar, CRM, IF logic, and Try-Catch in 2–3 hours.

Integration with Corporate Systems and APIs

In order for the AI worker to qualify as a true digital employee, it must have access to read/write capabilities to your CRM, ERP, spreadsheets, email, messenger systems, and billing software.

• Level 1—Out of the box applications: There are pre-built connectors in ASCN.AI, n8n, or Zapier that will allow you to build integrations with no programming.
• Level 2—HTTP Request: Send REST API requests directly, when a connector does not exist.
• Level 3—Webhooks: Event sync in real-time, bi-directionally.
• Level 4—API-less Parsing: Use web scraping (this poses risks and requires maintenance).

Full workflow: Recording all Telegram leads automatically into Notion CRM via the API tokens, via triggers from Telegram, via AI agent, and via HTTP requests with error handling using Try-Catch.

The Process of Training, Tuning, and Testing an AI Agent

To obtain quality results, AI agents must be iteratively prompted, tested with actual, real-world data and receive feedback.

1. Base Prompt (v1.0): Outline instructions on the agent's role, responsibility, work style and any limitations imposed on them.
2. Testing with Actual Queries: Record errors, assess success of queries, i.e. adequateness.
3. Prompt Refinement (v2.0): Add examples, function call rules, length of response.
4. Data Collection and A/B Testing: Customer satisfaction ratings, resolution rates, processing times, number of hallucinations.
5. Fine-tuning AI Models (Optional): Retraining the model using larger datasets of labeled data will improve accuracy and access to resources in the future.

At ASCN.AI fine-tuning the GPT-3.5-Turbo used for crypto arbitrage resulted in a 68% accuracy increase to 94%, a decrease in hallucinations from 12% to 1.8%, and a 40% reduction of latency.

Testing Tools: LangSmith, PromptLayer, Weights and Biases, and in-house dashboards with real-time alerts.

Examples and Case Studies of GPT Employees

Case Study 1: AI Assistant for Inbound Email

An executive receives approximately 80–120 emails each day. Approximately 60% of the emails received are spam, advertising or notifications. It takes the executive 40–60 minutes to sort through all received emails. Further, it takes the executive an additional 40 minutes to reply to received emails with standard responses. The solution is for an ASCN.AI agent to classify each email received as urgent, delegable, auto-reply or spam. Urgent emails will be sent to the executive via Slack (instant messaging), delegable emails will be assigned to the employee responsible (employee), auto-replied will be generated by the agent and spam will be deleted from the executive's inbox. Results: The email processing time has been reduced from 90 mins to 15 mins a day, with no loss in important emails over the past 3 months.

Case Study 2: Automatic Scheduling of Meetings

Coordinating a meeting usually takes 10–15 mins and 3–5 iterations of scheduling. The solution is to use an AI agent in Telegram to take the client’s preferences, check the Google Calendar for availability, and provide available time slots to schedule the meeting and send an invite. Coordination time has been reduced to 2–3 mins per meeting and the client satisfaction score was 9.1 out of 10.

Customer Support and Sales

Case Study 3: First Level Support for E-Commerce

An E-commerce site receives 200–300 support tickets per day with 70% of them being repetitive or similar. The solution is to use an AI agent who uses a vector knowledge database to provide responses to the support tickets and check status of customer orders in the system. Complex support tickets will be escalated to a human by providing an agent summary of the issue. After 3 months, approximately 68% of the support tickets were resolved using the AI agent, resulting in customer satisfaction score of 4.3 out of 5 and the company will save approximately 140 hours per month.

Case Study 4: Lead Qualification for B2B Sales

The sales department receives approximately 50–80 leads per week with 15–20% of them being qualified and the sales staff spend 2–3 hours per day qualifying leads. The solution is to use an AI agent to provide initial screening and scoring of leads and route qualified leads to the appropriate sales team member. The conversion rate of these leads has increased from 12% to 34% and time spent qualifying leads has decreased by 75%.

Creating Intelligent Assistants for Team Work

Case Study 5: AI Assistants for Project Teams

Developers ask for the same documentation 10–15 times per day. The Team Lead spends 1–2 hours per day answering these questions. The solution is to use an AI agent in Slack to do semantic searches in Notion, Confluence and GitHub, provide custom summaries, and collect daily standups from Jira. 19–24 seconds to respond versus 14–15 minutes; repetitive questions dropped by over 60%, and NPS is 9.8 out of 10.

Case Study 6: AI Assistant for Financial Control

In the case of AI assistants who provide financial controls, one founder had spent between 3–4 hours monthly doing so. Solution: Integrate banking APIs, Google Sheets, and Telegram to receive daily summaries of activity, notifications of reason for excessive spending in the previous week, and reminders for payments due soon. He now only needs about 15 minutes per week to perform financial control and reduce or eliminate any unnecessary spending by $1,200/month.

Advantages and Disadvantages of AI Employees

• Cost Reduction: Cost to process requests reduced from $2 to $5 for humans to $0.002 to $0.05, resulting in an ROI of 100x.
• Freeing Employee Time: Automating 5 to 10 tasks will give each member of the 5 person team 10 to 20 extra hours weekly.
• Scalability without Proportional Hiring: Net increase of 100% in the customer base may only require a decrease in staff of 10–15%, rather than 50–70% increase in staff size.
• Speed and Availability: Responsiveness of AI agents of 1 to 10 seconds, 24/7.
• Error Reduction: AI agents have much lower error rates in repetitive tasks, 98–99.5% accurate.
• Predictable Financing: AI agents allow companies to maintain a sustainability based on fixed U.S. costs of doing business rather than fluctuations with revenue generation.

Technical, Ethical Challenges, and Risks

Technical Limitations:

1. Falsehoods—hallucinations. Solutions: use RAG; human review; backup to an alternative service.
2. Failure of external APIs. Solutions: alternate data storage; cache.
3. Complex debugging; difficult to anticipate response to inputs. Solutions: store logs and mark responses; set temperature to 0; run regression tests.
4. Context—AI has a limited context to draw from and therefore, may not process entire history/sequence at one time without breaking into smaller groups and summarizing.
5. Transparency; users should be made aware they are talking to an AI employee.

Ethical and Regulatory Risks:

• Confidentiality: Between public clouds and private hosting options.
• Model Equity: Investigating & evaluating fairness as well as reaction time.
• Staff Development: Re-training your staff to help them not get laid off.
• Legal and Financial Liabilities: Disclaimers and liabilities - liability insurance and human in the loop for decision making requiring human reviews.

Frequently Asked Questions (FAQ)

Security and Data Privacy

How does a customer's data get sent? The cloud LLMs will forward it to the provider. In Enterprise opportunities, the provider will generally not keep this data on file. The best option would be for the customer to self-host because all data will be stored in security via encryption. If you are in a regulated industry, white-label will be available to you as well.

What types of protection are in place for prompt injections? The provider has secured all system prompts and is validating all incoming data. Role-Based Access Control (RBAC) is being used by the provider, and there is logging of all user activity, as well as heuristics/ML monitoring for potential cyber-attack events.

Does GDPR allow for processing of personal data? Yes; however, data must have explicit consent to process, and the provider must have a Data Processing Agreement (DPA), encrypt data created, ensure that all processing of data is auditable, and certified or self-hosted solutions must be used.

Technical Support and AI Agent Updates

What if the provider updates their model? You would first pin your API version, conduct frequent regression testing, utilize canary deployments to monitor the release of the new model at scale, and rely on ASCN.AI's support team in conducting any rollbacks or A/B testing.

How often do you update your prompts/knowledge bases? Normally, all system prompts will be reviewed every 90 days or any time there are any business process changes within your organization; all knowledge bases will automatically update in real-time based on webhook utilization and/or monitoring and auditing activity conducted every 7 days.

What does ASCN.AI provide for support? Basic level provides users documentation and support response in 24–48 hours; Pro/Enterprise levels included priority access to support, training, and custom development; and White Label levels include a separate state-supported account manager and guaranteed performance thresholds (SLA).

The Cost of Creating and Owning an AI Employee

Initial Costs: Platform - $29/month to $200/month; LLM API costs - approx $0.25–$0.50 per 1000 requests and integrations - $10 to $50 each would require 2–6 hours work or $300–$1500 for freelancer work; total initial cost is $50–$300 first month and after that completion, expects $40–$150 for the following 12 months.

Monthly Scaling Expenses: $50–$100 for 1,000 requests, $150–$400 for 10,000 requests, & $800–$2,500 for 100,000 requests; therefore your total savings for using AI instead of staffing to complete a given number of requests is significant (i.e. $600–$1200 for 10,000 requests).

Hidden Financial Costs: Initial training of employee; ongoing maintenance & updating of current capabilities; ongoing testing; and legal support related to industry regulation.

Conclusions and Implementation Recommendations

AI Employee is a real option to improve overall efficiency. Start small by selecting one repetitive task to automate with no code, do so in 1 week, measure results and recommend moving forward.

1. You should not try and do everything all at once. Your best option for highest return on invested time and money is to start with 1–3 tasks.
2. Involve your team. Document tasks characterized by unconventional thought processes and leverage those examples in developing your agent.
3. In addition to involvement of your team-based agents, you should conduct post-measurement KPI analysis compared to agent based performance, i.e., time to respond, CSAT, escalation rates, etc.
4. Each time an agent is implemented utilize Human-in-the-loop (HITL) methodologies for reviewing/approving/disapproving the output provided by the agent for at least 30 days.
5. Invest significantly in your knowledge base because it will represent 80% of the success/failure of agents utilizing your knowledge base.
6. Instead of large increases in employment/having significant reductions in your employee workforce, consider retraining existing employees to support the implementation of your new technology.
7. You need to be prepared for many iterations in moving from approximately 70% coverage to >=90% coverage and will typically take 2–3 optimizations cycles.
8. Documenting every aspect of the process will create an internal body of knowledge that could be leveraged internally.

The world is becoming more an AI-focused world every day. Today, the amount of information & support from a workforce that is always there will simply be limitless through virtual employees.

Дисклеймер

Информация в статье носит общий характер и не заменяет инвестиционных, юридических или консультаций по безопасности. Использование AI помощников требует осознанного подхода и понимания функций конкретных платформ.