AI agents are similar to computer programs in that they can execute tasks, but they’re even better: they can run by themselves.
That’s what makes them so powerful for companies that want to streamline customer service processes at scale.
Psycray recognizes the power of AI agents and uses them to make our clients’ lives better.
In this post, we’ll explore:
- What AI agents are.
- How they work through a process involving deep data, large language models, and complex learning.
- How AI agents are built, from data input to deployment.
- Real-life examples of how AI agents are used in work settings.
- How Psycray uses AI Agents.
- Concluding thoughts.
What Are AI Agents?
According to Salesforce, AI Agents are systems that can direct AI without human input. They rely on machine learning and natural language processing, and they’re made using an agent builder. An agent may help with any number of tasks, including but not limited to customer service, administrative processes, and answering frequently asked questions.
At Psycray, we take this a step further by tailoring AI chatbots to specific professional associations. Our chatbots process and understand Natural Language Processing technology for industry-specific inquiries. The technology interprets questions and searches database resources like professional development materials, certification details, and networking opportunities to deliver the most relevant information.
As an example, if a new member of an investment recovery association is overwhelmed by all the information out there and asks the chatbot for details on the industry’s best practices, the chatbot can deliver a clear and concise overview.
Similarly, if a member of the association wants certification details, a chatbot can quickly retrieve those for the member.
Through their ability to scour information and deliver targeted answers, AI agents can help people solve problems faster than ever, making them extremely valuable tools in today’s job market.
How do AI Agents Work?
Data Collection– An AI agent uses large volumes of data. Agents collect data from multiple sources, including but not limited to customer transactions, transaction histories, and social media posts to retrieve the most up-to-date information. In addition, advanced agentic AI integrates and processes the most recent information in real time.
Decision-Making – AI agents can use past interactions and current context to analyze data and come up with a reasonable response by using complex machine learning models.
Action and Execution – Once AI agents have gathered and analyzed all relevant data, they’re ready to take action based on the conclusions they’ve made. Goal-based agents act autonomously due to the programming and objectives they’re given. These could be answering a customer inquiry, processing a request, or raising a complicated issue to a human who can help. The execution is designed to be seamless and efficient so humans can get their problems solved in a timely, accurate manner.
Learning and Adaptation – An AI agent’s ability to learn and grow is a major part of what makes it an intelligent agent. As AI learns from previous interactions, it gets better and more efficient at solving similar problems over time. Autonomous agents operate based on what they learn. As they acquire more experience, they operate more efficiently.
How to Build an AI Agent
Learning to build AI agents effectively is essential to get them up and running. Here are the basic steps toward the deployment of AI agents:
1. Define job success and criteria in a one-page document with scope, users, constraints, and an evaluation checklist. Use 3-5 example questions with expected answers and supporting links.
2. Inventory and prep the document. Add the title, author, version, date, product, audience, and access level for the metadata.
3. Chunk data. Break large sections of content into smaller pieces.
When chunking data, it’s crucial to keep in mind the number of tokens you use. Tokens are the number of characters AI reads.
Have 200-800 tokens per chunk, and break on headings, sections, and bullets. Additionally, enrich each chunk with metadata, such as doc_id, section, version, and tags.
4. Build and keep the index up-to-date. Use a well-supported index model if you need it, and keep the same model version for consistency. Use a scheduled workflow that notices content changes, re-chunks and re-embeds only what is needed, gets rid of deleted documents, and records a sync log of what changed and when.
5. Use retrieval that actually retrieves. Utilize a hybrid search (BM25 keyword + vector similarity + metadata filters), query rewriting, and an optional LLM or encoder re-ranker to improve top-K quality. Vector retrieval often misses key terms, while hybrid retrieval catches both semantics and exact matches.
6. Answer your prompts only from the provided context. If something’s missing, say it’s missing. Cite sources with titles and section names. Only feed the top, de-duplicated chunks (Post-Re-Rank). Finally, ask for answer, Why this answer, Citations (doc + section), and Follow-ups.
7. Tools and Orchestration: n8n vs. LangChain:
n8n is effective for connecting storage, crawlers, translations, indexers, and scheduled syncs. It also helps with visual branching for guardrails and notifications. Lastly, it uses HTTP database, queue, and custom functions node to tie systems together.
n8n is the backbone of our AI work; it connects APIs, databases, and AI models into workflows. It’s a visual automation and orchestration platform that helps interlock all of our core components.
LangChain is perfect for building the RAG Chain — retrieval, reranking, prompt assembly, and output parsers, and there are many adapters for vector stores and LLMs, so they’re simple to swap out and test. It’s a framework for connecting data, tools, and structured logic, and we use it when we need more control or customization than what n8n can offer.
Put another way, use n8n for pipelines and operations, and use LangChain inside your API/App to serve queries.
8. The next step, permissions and safety, is non-negotiable. Use authentication to ensure the user’s identity. Filter retrieval by the users permitted doc_ids and groups. Third, redact in logs, never repeat credentials. Lastly, if a source is restricted, don’t reference or summarize it..
Pro tip: Be sure to filter at retrieval time instead of after RAG. If you don’t, you could leak.
9. Evaluate your agent. Include 50-200 real questions with authoritative answers and allowed sources. Offline, measure answerability, groundedness, faithfulness, latency, and cost. Online, sample live traffic for human rating, and do A/B prompts of two sets of code and test each version. These tests will help you verify your agent’s trustworthiness pre-launch.
10. Observability and product feedback.
Look at your agent’s logs, dashboards, user loops, and auto-tickets.
11. Cost, latency, and reliability tuning.
Look at Top-K discipline, using re-ranking to maintain a small context. Cache embeddings and regular answers with TTL and version checks.
12. Ship your agent like a product once all previous steps are complete. You’re only finished when the ops, evals, and rules are in place.
How AI Agents can work in real-life scenarios
AI agents can observe situations, act on them, and learn from experience. This is what makes them so valuable to various industries; they can understand, respond to, and learn from interactions to enhance customer service for everyone.
With that said, here are three ways AI agents can be helpful in the workforce:
Scenario 1: Healthcare – A patient would like to schedule an appointment and has a few questions about their medical history. A patient services agent can not only schedule an appointment for the patient, but also help them pick the best physician for their needs, review coverage benefits, generate medical history summaries, and approve care requests, according to Salesforce.
Scenario 2: Insurance – Insurance AI agents can extract and process data from documents, emails, and databases. They can also detect fraud and anomalies. Lastly, they can be scaled to handle increasing volumes of customers and data, making them a huge asset to any insurance company, according to Salesforce.
Scenario 3: Retail – A customer wants a pair of shoes. AI agents can offer the customer personalized recommendations and even give them a personal assistant drawn from trusted customer data, according to Salesforce.
As you can see, AI agents offer multiple applications in business settings, making them a huge help in the workplace.
Psycray’s Experience With AI Agents
At Psycray, we built an AI agent chatbot for the automotive and e-commerce industries. It uses advanced Natural Language Processing technology to understand customer questions. It also interprets language and searches its vast inventory, such as the Tire Decision Guide, to offer the most relevant answer in a conversational tone. This helps the shopper get their question answered quickly and accurately.
Here are three examples of the kinds of inquiries our AI chatbots can address:
Example 1:
Q: I need help choosing the right tires for my car. Can you assist?
A: Absolutely! I can guide you through the Tire Decision Guide to find the perfect tires in under two minutes. Let’s start with your vehicle make and model.
Example 2:
Q: Are there any current promotions or special offers available?
A: Yes! We offer free shipping on orders over $50, which includes tires, wheels, brakes, suspension parts, and accessories. Would you like to see the latest promotions on tires?
Example 3:
Q: How can I track my order?
A: You can track your order by entering your order number on our tracking page. Would you like me to direct you to the tracking page?
Conclusion
At Psycray, we’re not only adopting AI but embracing it as a core part of our solution strategies.
Agent Mode helps ensure trust and frees us up to focus on strategy. When built properly with clean data, AI agents can validate formulas, fix errors, and explain assumptions, which makes them trustworthy and useful.
Agentic AI is the foundation of every solution we create for our clients. Whether it’s a customer chatbot assistant or complex data flows in our website development solutions, we bake quality AI solutions into everything we do.
Overall, our AI agents provide a more efficient workflow and a more seamless customer experience. If you would like more information about how AI agents can help your team, contact us.