Skip to content

AI Agent Development

Agents that do real work inside your systems, with guardrails, evaluations and a human in the loop.

Talk to an expert

Overview

Most companies have tried a chatbot. Far fewer have an agent that reliably finishes a task: reads the ticket, checks the order system, drafts the refund, and asks a person to approve it. We design and build agentic systems on current frontier models, connect them to your tools through APIs and the Model Context Protocol (MCP), and ship them with the evaluations, permissions, audit logs and cost controls that production needs.

Challenges we solve

  • Agent demos that work once but fail on real, messy inputs
  • No safe way to let an AI act on business systems without risk
  • No evaluation suite, so nobody knows whether a prompt change made things better or worse
  • Model costs and latency that grow faster than usage

What we deliver

AI Agent Development capabilities

Agentic workflow design

We map the task, decide where an agent adds value and where a plain rule is better, and design tool-calling flows with clear approval steps.

MCP servers and tool integrations

MCP servers and typed tool APIs that let agents read and act on your CRM, ERP, ticketing, email, WhatsApp and internal databases, with scoped permissions.

Multi-agent orchestration

Planner, worker and reviewer agents that split long tasks, with shared memory, retries and hand-off to a human when confidence is low.

Evaluations and guardrails

Test sets built from your real cases, automated scoring, prompt-injection defences, PII redaction and policy checks before any action runs.

Observability and cost control

Tracing for every model and tool call, per-task cost tracking, model routing and caching so the agent stays fast and affordable.

Voice and messaging agents

Agents that work where your customers are: WhatsApp, web chat, email and voice, with the same rules and audit trail across channels.

Process

How we work

  1. 01

    Discover

    Pick one high-value task, collect real examples and define what "done well" means.

  2. 02

    Prototype

    A working agent on your data in two to three weeks, measured against the test set.

  3. 03

    Harden

    Guardrails, approvals, permissions, monitoring and load testing.

  4. 04

    Operate

    Launch, watch the metrics, and keep improving with new evaluation cases.

FAQ

AI Agent Development: common questions

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

A chatbot answers questions. An agent takes actions: it calls tools and APIs, works through several steps, and finishes a task such as updating a record or drafting a reply for approval.

Which AI models do you use?

We are model-agnostic. We pick from current frontier and open-weight models based on accuracy on your test set, cost, latency and data-residency needs, and we can route between them.

How do you keep agents safe when they can act on our systems?

Scoped permissions, human approval for sensitive actions, input and output guardrails, full audit logs, and an evaluation suite that runs before every release.

How long does a first agent take?

A focused first agent usually reaches a measured pilot in four to eight weeks.

Related

Services that work well together

Generative AI & LLM Development

RAG systems, AI copilots and LLM-powered product features, built on your data and shipped with evaluation and monitoring.

Learn more

AI Business Process Automation

Automate back-office work across email, WhatsApp, spreadsheets, CRM and ERP with AI workflows and clear human approval steps.

Learn more

WhatsApp Business API & Meta Integration

WhatsApp Cloud API setup, Embedded Signup, CRM integration, chat automation and AI agents on the official Meta platform.

Learn more

Ready to talk about ai agent development?

Tell us about it. In a free 30-minute call we will tell you honestly what AI can do, what it costs and how fast you can see results.

Book a consultation hello@invigosol.com