AI Agents and Chatbot Development

PS3G is an AI agent development company and chatbot development company for organizations of every size. We build customer-service and internal agents that answer from your own documents, take actions in your systems through approved tools, hand off to a person when they should, and report the numbers that tell you whether they are working. A founder might need an AI chatbot for a website that answers product questions and books demos. A growing company might want support triage that drafts replies inside its helpdesk. A department in a larger organization might need an internal assistant over policies and procedures that respects existing permissions. The build is scoped to the use case; the guardrails come as standard.

What an AI agent is, in business terms

A chatbot answers questions. An AI agent gets things done. Both are built on large language models, but an agent is also given tools (look up an order, create a ticket, check a calendar, send an email), a goal, and rules about when to act and when to ask. The agent decides which tool to call, reads the result, and continues until the task is complete or it needs a person. That capability is also what makes agentic AI development a governance question as much as an engineering one: an agent with the wrong tools, or no limits, can act at scale.

Type	What it does	Typical fit
Scripted chatbot	Follows a decision tree of fixed answers	A handful of predictable questions; no model cost
AI chatbot	Answers in natural language from your content, with citations	Website help, internal knowledge assistants, first-line support
AI agent	Uses tools to complete tasks across systems, with a person in the loop	Order and appointment handling, support resolution, document workflows
Workflow automation	Runs a fixed sequence of steps, with AI used for individual decisions	Back-office processing, covered by our AI and RPA intelligent automation service

Use cases that work today

• Customer support triage and resolution. Classify incoming tickets, answer the routine ones from your knowledge base, draft replies for the rest, and escalate anything with a refund, complaint or legal flavor. AI customer service automation done this way reduces queue time without removing people from hard cases.
• Internal knowledge assistants. An assistant over HR policies, IT procedures, sales playbooks or engineering docs, with permission-aware retrieval so staff see only what they are allowed to see.
• Order and appointment handling. Check availability, book, reschedule or cancel, confirm by email or SMS, and write the result back to your scheduling or order system.
• Document workflows. Read an inbound application, invoice or form, extract fields, check them against your records, and route exceptions to a person with the reasons attached.
• IT helpdesk. Password resets, access requests and common fixes handled through approved tools, with a ticket created and a person looped in for anything unusual.
• Sales qualification. Answer inbound questions, collect the details your team needs, score the lead against your criteria and book a call, writing everything to the CRM.

What does not work well yet: agents given open-ended authority over money, contracts or customer data without review, and agents expected to replace a process nobody has documented. Our guide to AI agents for business covers the pattern in more depth.

How we build agents

1. Tools and data access. Each action the agent can take is a typed tool with a narrow contract, exposed through function calling or the Model Context Protocol (MCP), and authorized with the user’s identity rather than a shared account. Retrieval over your documents is permission-aware. Where an API does not exist, our API development and integration team builds it.
2. Guardrails. An allow-list of tools per agent, confirmation steps before anything irreversible (a payment, a deletion, an outbound message), limits on steps and tokens per task, input and output content filtering, prompt-injection defenses for untrusted content, and schema validation on every tool call.
3. Human hand-off. Clear triggers (low confidence, a sensitive topic, a frustrated customer, an out-of-scope request) that move the conversation to a person with the full transcript, the retrieved sources and the actions already taken, inside the helpdesk or CRM your team already uses.
4. Evaluation. A scenario suite built from real conversations, scored for task completion, correctness, policy compliance and tone before every change, with model-graded evaluation calibrated against your reviewers.
5. Logging and audit. Every message, retrieval, tool call and hand-off is traced and retained according to your policy, so any outcome can be explained and any pattern of failure can be found.

Scope a pilot agent around one use case and one measurable outcome.

Chatbots for websites and apps

Custom chatbot development covers every channel your customers and staff already use, connected to one set of tools and one knowledge base so answers stay consistent.

• Web chat. An AI chatbot for your website or web application that answers from your content with citations, captures leads and books calls, built to match your brand and accessibility standards alongside our website development service.
• WhatsApp and SMS. Conversational support and notifications through the WhatsApp Business API and SMS gateways, with opt-in and consent handling built in.
• Voice. An AI voice agent on your phone line, using speech-to-text and text-to-speech with telephony integration, for appointment handling, order status and first-line support, with a warm transfer to a person when needed.
• Teams and Slack. Internal assistants where your staff already work, with single sign-on and audit logs.

Platforms and when to use each

Platform	Best for	Trade-offs
OpenAI or Azure OpenAI with an agent SDK	Custom agents with strong tool calling, structured outputs and full control over orchestration	You own the hosting, evaluation and monitoring; Azure adds private networking and data-handling commitments
Microsoft Copilot Studio	Organizations on Microsoft 365 that want an AI agent builder with connectors to SharePoint, Dataverse, Teams and the Power Platform	Fast to start and well governed through Microsoft 365 controls; less flexible for complex custom logic
Open frameworks (LangGraph, Microsoft Agent Framework and Semantic Kernel, Google ADK, provider agent SDKs)	Multi-step agents, multi-agent workflows, and portability between model providers including Gemini, Claude and open-weight models	More engineering responsibility; the right choice when the agent is part of your product

We choose based on where your data and identity live, how complex the task is, and who will maintain the agent after launch. Many organizations end up with both: Copilot Studio for internal assistants and a custom build for customer-facing or product-embedded agents. The build itself is delivered by our AI development team; if the use case is not yet chosen, our AI consulting team runs the readiness and prioritization work first.

Governance, privacy and cost control

Every agent is recorded in your AI inventory with its purpose, tools, data sources, risk rating and owner, and its controls are mapped to your policy through our AI governance service. Personal data is minimized and redacted where it is not needed, retention is set to your rules, and model providers are chosen for their data-handling terms, with self-hosted open-weight models available for the strictest cases.

Run cost is driven by conversation length, the number of tool calls and retrievals per task, and the model tier. We keep it predictable by routing simple turns to a small model, caching repeated context, capping steps per task, resolving routine requests without a model where a rule will do, and reporting cost per resolved conversation from the first week of the pilot.

Engagement models

• Fixed-scope project. A pilot agent for one use case, typically scoped in weeks rather than quarters, with a measurement plan and a fixed-scope quote. Scale after the numbers hold.
• Dedicated team or staff augmentation. Agent, integration and evaluation engineers working inside your team on a monthly basis.
• Managed services. We run the agents you launched under defined SLAs: monitoring, evaluation, knowledge updates, model changes and cost reporting.
• Advisory. Architecture and guardrail review for an agent your own team is building.

How we start: a first conversation, a short assessment, and a written proposal.

Why PS3G as your AI agent development company

• We ship AI features inside our own HIPAA-compliant EHR and telehealth platform, DocVilla, so guardrails, audit logging and permission-aware design are things we operate every day, not slides.
• Over a decade of software development and IT services, with a US headquarters in Wilmington, Delaware and a delivery center in India.
• Microsoft-certified and open-source-certified engineers, so Copilot Studio, Azure OpenAI and open frameworks are all available to you.
• A testing center of excellence that builds the scenario suites agents are measured against.

Related services

• AI development: RAG, integration, evaluation and the models behind every agent.
• AI consulting: picking the first use case and checking readiness.
• AI governance: inventory, risk assessment and policy for agents.
• AI and RPA intelligent automation: AI workflow automation for back-office processes.
• API development and integration: the tools agents call.
• Website development: the sites our web chatbots live on. See all PS3G services.

Guides on this topic

• AI Agents for Business: Use Cases and How to Build One Safely
• AI Workflow Automation: 15 Examples by Department (and What They Need)
• AI Solutions for Small Business: What Works and What It Costs

Frequently asked questions

What is the difference between an AI agent and a chatbot?

A chatbot answers questions; an agent completes tasks. An AI chatbot can explain your return policy from your documents. An AI agent can look up the order, check it against the policy, start the return in your system, send the confirmation and hand off to a person if the customer is unhappy. Agents need tools, limits and hand-off rules, which is why they are designed and governed differently.

Can an AI chatbot for a website use our own documents?

Yes. We index your pages, PDFs, help articles and policies, retrieve the relevant passages for each question and have the model answer from them with citations. Content stays current as you update it, nothing is trained into the model, and we can restrict which sources are used. For internal assistants the same retrieval respects your existing permissions, so staff see only what they are allowed to see.

How do you stop an agent from doing something it should not?

By design rather than by hope: a short allow-list of tools, each with a narrow contract; confirmation steps before irreversible actions; limits on steps and tokens; filtering of inputs and outputs; defenses against instructions hidden in documents or web pages; and a hand-off rule for anything uncertain. Every action is logged, and a scenario suite is run before each change to catch regressions.

Should we use Microsoft Copilot Studio or a custom build?

Copilot Studio is a good fit when your data and users are in Microsoft 365, the use case is internal, and you want a low-code AI agent builder your own team can maintain. A custom build fits customer-facing agents, agents embedded in your product, complex multi-step logic, or a need to switch between model providers. We often recommend both for different use cases.

What does AI customer service automation cost to run?

Run cost depends on conversation volume and length, how many tool calls and document retrievals each conversation needs, and the model tier. During the pilot we measure cost per resolved conversation alongside resolution rate and hand-off rate, then design toward a target using routing, caching and step limits. We quote the build as a fixed scope and show you the projected run cost before you commit.

Can an AI voice agent answer our phones?

Yes, for defined tasks such as appointment booking, order status, store hours and first-line support. The voice agent uses speech recognition and synthesis with your telephony provider, calls the same tools as your chat agent, and transfers to a person with context when the caller asks or the task falls outside its scope. We test it on recorded scenarios, including accents and background noise, before it takes live calls.