AI Agent Development for Real Business Workflows
An agent is only useful when it can safely take action in your systems. We build agents with clear tools, guardrails and hand-off points — for chat, voice and phone.
The problem
Generic agent frameworks make it easy to wire an LLM to tools and hard to predict what it will do next.
In real workflows — taking an order, updating a CRM, booking a slot — an agent that improvises is a liability.
The approach
We design the workflow first, then give the model a narrow set of well-typed tools and explicit states it moves between.
Every action is logged, risky steps require confirmation, and there is always a path to a human.
What we build
- Voice and phone agents
- Inbound and outbound calling agents using telephony APIs and speech models.
- Tool-using assistants
- Agents that read and write to your CRM, database, calendar or internal APIs.
- Multi-step workflow agents
- Agents that plan and execute defined processes with checkpoints.
- Internal team agents
- Assistants that work with your team's codebase, documents and tools.
Use cases
- Phone ordering for restaurants
- Lead qualification calls
- CRM data entry from conversations
- Internal support assistants
Typical architecture
Channel (chat, voice, phone)
Agent orchestrator
LLM
Typed tools
Your APIs and database
Audit log + human hand-off
Technology
- Claude API (tool use)
- Telnyx
- ElevenLabs
- Python / FastAPI
- Laravel
- PostgreSQL
- Redis queues
Related work
Process
- 01
Understand
Understand the business problem and the outcome you need.
- 02
Architect
Choose the AI architecture, integrations and technical approach.
- 03
Prototype
Validate the critical workflow before building around it.
- 04
Build
Develop the product in milestones you can review.
- 05
Validate
Test quality, performance, reliability and edge cases.
- 06
Launch
Deploy the production system and monitor it.
- 07
Improve
Optimise using real usage and feedback.
Questions
Can an agent handle phone calls?
Yes. Voice agents combine telephony, speech recognition, an LLM and speech synthesis. Latency and interruption handling are the main engineering work.
How do you stop an agent doing something wrong?
Narrow tools, validated inputs, explicit states, confirmation before irreversible actions and full logging.
Do we need a framework like LangChain?
Not necessarily. Many production agents are clearer as a small, explicit orchestrator using the model provider's native tool use.
Further reading
Tell us what you want to build.
Send a short brief. We'll reply personally with questions or a suggested next step — usually a technical discovery call.