Custom AI agents with tools, guardrails, and a human escape hatch
Mopshy AI builds production AI agents for businesses across the United States, remotely from Pittsburgh, PA — agents that take real actions in your systems and hand off cleanly when they should not.
An AI agent development company builds an agent that can actually do things: read from and write to your systems through defined tools, follow the policies your business runs on, escalate to a person when confidence or authority runs out, and be evaluated before it touches a customer. The hard parts are not the model — they are the tool boundaries, the escalation rules, and the evaluation set that proves the agent behaves before it goes live.
Who this is for
- Businesses missing calls, chats, or form submissions outside working hours and losing the enquiry entirely.
- Teams whose off-the-shelf chatbot cannot answer account-specific questions because it cannot see the CRM or the booking system.
- Operations leads drowning in intake: qualifying, triaging, and routing requests that follow a knowable pattern.
- Companies that need agent behaviour to be auditable — who said what, on which policy, with which data.
When an agent is the wrong answer
- The workflow is fully deterministic. A rule-based automation is cheaper, faster, and easier to defend.
- There is no reliable source of truth for the agent to read from. Fix the data first; an agent amplifies bad records.
- Nobody can be assigned as the escalation owner. An agent without a human backstop is a liability, not an asset.
What we build
Voice agents
Answering, qualifying, and booking on inbound calls, with a defined transfer path to a person and a written transcript attached to the record.
Chat and web agents
Site and portal agents that answer from your own content and system data rather than guessing, and that capture the enquiry when they cannot answer.
Email and inbox agents
Triage, classification, drafting, and routing on shared inboxes, with a review step wherever the reply commits the business to something.
Intake agents
Structured capture for enquiries, applications, or service requests, validating as they go and writing clean records into the system of record.
Tools and actions
Each capability exposed as an explicit tool with a typed schema and a permission boundary. The agent cannot take an action nobody defined.
Evaluation harness
A test set of real and adversarial cases, run before every prompt or model change, with pass thresholds agreed up front.
How an agent engagement runs
Agents ship behind a gate. Nothing faces a customer until it passes the evaluation set and has a working escalation path.
Scope and policy capture
What the agent may say, may do, and may never do. Written as explicit policy, because that is what the prompt, tools, and evaluations are all derived from.
Build and integrate
Agent, tools, and integrations built against your systems, with logging on every tool call from the first day.
Evaluate
Test set assembled from real historical cases plus adversarial ones. The agent is scored on correctness, refusal behaviour, and escalation accuracy — not on demo vibes.
Staged rollout
Shadow mode or a limited channel first, with human review of transcripts, then a widening rollout as the numbers hold.
Operate and improve
Monitoring, transcript review, regression runs on every change, and a rollback path that works. Or a full handover if you are taking it in-house.
Integration and tool design
An agent is only as useful as the tools it can call, and only as safe as the boundaries on those tools.
- Read tools scoped to the minimum records required to answer, never a blanket database credential.
- Write tools with explicit schemas, validation, and idempotency so a retried action does not double-book.
- CRM, calendar, phone, billing, and job or case management systems wired through documented contracts.
- Knowledge retrieval from your own approved content, with the source attached to the answer.
- Escalation channels — transfer, ticket, or notification — carrying the full conversation context.
- Structured logging of every tool call, input, and output for audit and debugging.
Guardrails and evaluation
Explicit refusal behaviour
The agent is built to say it does not know and route onward, rather than to produce a confident answer it cannot support.
Authority limits
Actions that commit money, contracts, or clinical or legal judgement require a human. That boundary is enforced in the tools, not just requested in the prompt.
Regression testing
The evaluation set runs on every prompt, model, or tool change. A change that lowers the score does not ship.
Full transcript retention
Conversations and tool calls are logged so any outcome can be reconstructed and reviewed.
Ownership and handover
Prompts, tool definitions, evaluation sets, and infrastructure config are delivered to you in editable form.
Questions buyers ask
What is the difference between an AI chatbot and an AI agent?
A chatbot answers. An agent acts: it calls tools, reads and writes records in your systems, and follows a policy about when to stop and involve a person. That action capability is what makes tool design, permissions, and evaluation the core of the work.
How do you stop an AI agent from making things up?
Three mechanisms together: retrieval from approved sources so answers cite something real, tool boundaries so the agent cannot invent an action, and an evaluation set that scores refusal and escalation behaviour explicitly. No single technique is sufficient on its own.
When does the agent hand off to a human?
Whenever confidence is low, the request falls outside the defined policy, the customer asks for a person, or the action exceeds the agent's authority. The handoff carries the full transcript and any records the agent touched.
How long does it take to build a custom AI agent?
It depends on how many systems the agent must touch and how strict the policy is. A single-channel agent with two or three integrations is a materially shorter build than a multi-channel agent with write access across your stack. Scope and timeline are fixed in writing before the build starts.
Do we own the agent and its prompts?
Yes. Prompts, tool definitions, evaluation sets, and deployment configuration are handed over and run in accounts you control.
Do you work with businesses across the United States?
Yes. Mopshy AI works remotely from Pittsburgh, PA with clients throughout the United States.
Related pages
Next step
Bring the conversation you want handled — a call, a chat thread, an inbox — to the free audit. We will map what a safe agent version of it looks like.