Custom AI Agent Development for Business

What a custom AI agent is, and what it is not

A chatbot answers questions. An agent does work. The difference is that an agent can call tools: look up a record in your CRM, create a work order, send an email through your account, post a journal entry, or ask a human to approve a step it is not allowed to take alone. Tepia builds agents for businesses that have a repetitive, rules heavy workflow where a person currently reads something, decides something and then updates three systems.

Tepia is careful about the word. An agent that can take actions inside your systems is a piece of business software with a language model in the loop, and it needs the same discipline as any software that touches money or customers. That means explicit permissions, logging of every action, a way to stop it, and tests that show how it behaves on real cases before it is trusted with live ones.

Agents built by Tepia run on Anthropic and OpenAI model APIs, with tool definitions written against your own REST and GraphQL endpoints and the SaaS systems you already use. If you are not yet sure whether you need an agent or a simpler AI feature, start with AI strategy consulting.

Agent use cases by department

The strongest agent candidates share three traits: the inputs arrive as text or documents, the rules can be written down, and the actions are reversible or can be gated by approval. Tepia uses the table below in Discovery to find the first workflow worth automating.

Tepia recommends launching every agent in a supervised mode where a person approves its actions, then widening its autonomy as the action log shows it is reliable. That approach gets a working agent into production quickly without betting the business on a first version.

Guardrails and evaluation: how Tepia makes agents safe to run

The agent itself is a few hundred lines of orchestration. The work that makes it trustworthy is the scaffolding around it, and this is where Tepia spends most of the engineering effort.

• Permission scopes. Each tool the agent can call is granted explicitly, with read and write separated. The agent’s credentials are its own, so you can see and revoke what it did.
• Approval steps. Actions above a threshold (money, customer facing messages, deletions) pause for a human in a review queue that Tepia builds into your existing tools or a small web console.
• Action log. Every step, tool call, input and output is recorded, so any outcome can be traced and replayed.
• Evaluation suite. Tepia assembles 50 to 200 real cases from your history with expected outcomes and runs the agent against them on every change. The pass rate is reported, not guessed.
• Kill switch and fallback. One setting disables the agent and routes work back to the human process.

Data handling follows the same practices as Tepia’s other AI work: retrieval scoped to what the agent may see, vendor accounts configured so your data is not used for training, HIPAA aware architecture where PHI is involved, and GDPR and CCPA deletion paths. Tepia also builds chatbots trained on company data, which are often the read only first step before an agent is given write access.

How Tepia approaches custom AI agent development

Tepia applies its six phase process to agents with extra weight on Discovery and testing. Discovery includes a system investigation of the systems the agent will act in (their APIs, permissions and rate limits), user interviews with the people doing the workflow today, and a third party integration review covering model vendors and SaaS terms. Deliverables are an Investigation Summary, an Interview Summary and User Stories written as the agent’s job description: triggers, decisions, actions and escalation rules.

Design for an agent means the human side: the approval console, the notifications, how the agent explains what it did, and how a person corrects it. Tepia delivers wireframes and sample designs for these surfaces, because an agent nobody can supervise is an agent nobody will trust.

Development and Testing runs on Alpha and Beta schedules. Alpha runs the agent in shadow mode, where it proposes actions but takes none, and Tepia compares its proposals with what staff actually did. Beta runs supervised with approvals. Training teaches your team to read the action log and tune the rules. Launch widens autonomy step by step with Tepia support reps watching.

A Tepia project manager is assigned to every engagement, with US based design and engineering leadership and engineers working in US hours. Process details are at tepia.co/process.

Why businesses choose Tepia to build agents

Most AI development companies are new. Tepia has thirteen years of disciplined engineering behind its AI work, and the agents it builds sit on the same foundation as the mobile apps, web portals and integrations it has delivered for manufacturing, healthcare facilities, grocery, field service and IoT clients. That matters because an agent is only as good as the systems it can reach, and Tepia already builds and integrates those systems.

Tepia also designs for the people around the agent. Emotionally intelligent engineering here means an agent that says what it did, asks when unsure, and makes its supervisor’s job easier rather than noisier. As the Newport Medical Solutions CIO said of Tepia, “We were impressed with Tepia’s genuine passion for their craft.” See examples at tepia.co/our-work and the full service list at tepia.co/services.

Frequently asked questions