AI agent development services for agents that do real work.
Hoop Interactive builds custom AI agents that answer calls, qualify leads, update your CRM and clear back-office tasks. Each agent gets only the tools it needs, asks a person before risky actions and is measured on tasks completed, not conversations had.
Chatbot, automation, or AI agent?
Most “AI agent” projects only need one of the other two. We'll tell you which, even if it's the cheaper option.
| What it does | Example | |
|---|---|---|
| AI chatbot | Answers questions from your content. It talks, but it doesn't act. | "What are your opening hours?" |
| Workflow automation | Runs the same fixed steps every time. Fast and cheap when nothing varies. | New form entry creates a CRM contact |
| AI agent (this page) | Works toward a goal, decides the next step, uses your tools and handles cases that don't fit a script. | Calls a new lead, qualifies them, drafts a quote and books a follow-up |
AI agents we build.
Custom AI agent development for one workflow at a time, then connected into a team of agents once each one proves itself.
Voice AI agents
Realtime phone agents for inbound calls, callbacks and appointment booking.
Sales & lead qualification agents
Follow up every lead in minutes, qualify them and hand hot ones to sales.
Customer support agents
Resolve tickets end to end: look up orders, process changes, escalate the rest.
Back-office & operations agents
Read documents, enter data, reconcile records and chase missing information.
Research & reporting agents
Gather data from the web and your systems, then write the weekly report.
Multi-agent systems
Specialist agents that hand work to each other, coordinated by an orchestrator.
Agentic features in SaaS
Let your users hand tasks to an agent inside your product.
AI agent consulting
Pick the right first workflow, map it and forecast savings before you build.
How much should your agent do on its own?
You choose the level for each action. Agents earn more autonomy as their success rate is proven. Limits, approvals and permissions are enforced in code outside the model, so a confused prompt can't talk the agent past them.
| Level | What the agent does | Example |
|---|---|---|
| Suggest (most start here) | The agent drafts and recommends. A person does every action. | Drafts a reply for your support team to send |
| Act with approval | The agent prepares the action and a person approves it in one click. | Sends a quote once a broker taps Approve |
| Act within limits | The agent acts alone below set thresholds and asks above them. | Refunds under $50 automatically, asks above |
| Autonomous | The agent runs routine work end to end, with sampled human review. | Calls every new lead within five minutes |
What's inside a reliable AI agent.
The language model is the brain. These six parts make it dependable enough to trust with real customers.
LangGraph
OpenAI Agents SDKAI agents we've built and deployed.
A Python and FastAPI bridge streaming live call audio between 3CX and OpenAI's Realtime API.
AGENTIC AI · ENTERPRISE TELEPHONY
An agentic voice AI connected to an enterprise phone system
The client wanted an AI agent to handle vehicle insurance sales callbacks on its existing 3CX PBX. We built a Python and FastAPI bridge over persistent WebSockets, including audio rate conversion, shipped in Docker.
Read the case studyHow we differ from a typical AI agent development company.
Most agent pages promise "autonomous agents that learn". Here is what that means in practice with us.
| What matters | Typical agent vendor | Hoop Interactive |
|---|---|---|
| Proof | Demo agents for made-up use cases. | Voice agents built for real insurance platforms, with case studies. |
| Scope | "Automate everything" from day one. | One workflow first, proven, then expanded. |
| Control | Fully autonomous, or nothing. | Autonomy level set per action, with approval gates in code. |
| Measurement | Number of conversations. | Task success rate, escalations and cost per task. |
| Integration | Works in a chat window only. | Connected to your phone system, CRM, WhatsApp and APIs. |
| Lock-in | Built on the vendor's closed platform. | Your code, your accounts, any model. |
From one workflow to a working agent.
Most single-workflow agents go from mapping to supervised launch in 4 to 8 weeks.
Map the workflow
We sit with the people doing the work today, map every step and decision, and pick the agent's exact job.
Week 1 · Blueprint & forecastConnect tools & data
Secure access to the systems the agent needs, with read and write permissions agreed action by action.
Weeks 1–2 · Permission matrixBuild in a sandbox
The agent runs against test accounts and recorded scenarios, so nothing touches real customers yet.
Weeks 2–5 · Agent on stagingEvaluate
We score the agent on real scenarios, including the awkward ones, until it passes the agreed success rate.
Weeks 4–6 · Evaluation reportSupervised launch
The agent goes live on a share of real work with human approval on key actions and daily review.
Weeks 6–8 · Live dashboardExpand autonomy
As results hold, we raise limits, remove approvals where safe and add the next workflow.
Ongoing · Monthly reviewAI agent development cost and timelines.
Start with one workflow. Each stage has a fixed price, and running costs are forecast per task. Model, telephony and hosting fees are billed to your own accounts.
- Investment
- from $3,000
- Timeline
- 1–2 weeks
- Output
- Workflow mapping with your team
- Included
- Tools, permissions, autonomy level
- Yours to keep
- Savings & cost forecast
- Investment
- from $10,000
- Timeline
- 4–8 weeks
- Build
- Chat or email agent with tool access
- Included
- Up to 4 integrations, guardrails
- Support
- Supervised launch included
- Investment
- from $20,000
- Timeline
- 6–12 weeks
- Build
- Realtime voice on Twilio or your PBX
- Scope
- Multiple specialist agents
- Support
- Monthly tuning for 3 months
Frameworks and integrations behind our agents.
Model-agnostic and built on open standards, so your agent isn't tied to one vendor.
Anthropic Claude
Google Gemini
HubSpot
Google Workspace
Slack
Stripe
PostgreSQLAI agents by industry.
Where agents save the most time for the businesses we work with.
Insurance
Lead callbacks, renewals, document collection and claim status updates.
Ecommerce & Retail
Order changes, returns, stock questions and abandoned cart follow-up.
Home & Property Services
Answer enquiries, book surveys and send quotes after hours.
Health & Wellness
Appointment booking, reminders and member questions, without medical advice.
Professional Services
Client intake, document checks and research for proposals.
SaaS & Startups
Onboarding agents, support agents and agentic product features.
Explore more AI services.
AI agent development pairs naturally with these services from Hoop Interactive.
AI Development
The broader practice this service sits under.
ExploreGenerative AI Development
LLM applications and copilots beyond agents.
ExploreAI Chatbots
Answers, not actions — when that’s all you need.
ExploreCustom AI Assistants
Agentic assistants that plan multi-step tasks.
ExploreChatGPT Integration
Function calling into your existing tools.
ExploreAPI & Integrations
The connective layer every agent runs on.
ExploreMarketing Automation
The workflow-automation alternative to an agent.
ExploreSoftware Development Outsourcing
A full team to build and run your agents.
ExploreAI agent development questions
About our AI agent development services. Ask us anything else.
An agent blueprint for one workflow starts from $3,000. A production agent for a single workflow starts from $10,000, and voice agents or multi-agent systems from $20,000. Running costs for model usage and telephony are forecast per task before you approve the build.
A single-workflow agent usually takes 4 to 8 weeks from mapping to supervised launch. Our AI sales call agent for a UAE insurance platform was delivered in one month, and a 3CX voice agent integration in two months.
A chatbot answers questions. An AI agent works toward a goal: it plans steps, uses tools such as your CRM, phone system or database, and takes actions like booking a call or updating a record, within limits you set.
Agents only get the tools and permissions their job needs. Risky actions such as refunds, sending quotes or deleting data require human approval, spending and rate limits are enforced in code, and every step is logged so it can be reviewed or reversed.
We choose per project. We build with Python and FastAPI, the OpenAI Agents SDK and Realtime API, LangGraph for multi-step workflows, and Model Context Protocol to connect tools, and we can work with Claude or Gemini models when they perform better.
Yes. We have connected realtime voice agents to a 3CX PBX over WebSockets and to Twilio telephony, so the agent can place and answer calls on your existing numbers and hand calls to a person.
We agree a task success rate on real scenarios before launch, then track completed tasks, escalations to humans, errors, time saved and cost per task on a live dashboard.
Agents use enterprise model APIs that do not train on your data, see only the records their permissions allow, and can run on private cloud deployments. Sensitive fields can be redacted before any model call.
Yes. You own the code, prompts, tool definitions and evaluation scenarios, and API accounts are set up in your name, so you can run, change or move the agent at any time.