Agentic AI Development

Agentic AI development: AI agents that complete the work, not just answer.

Autonomous agents that break a goal into steps, use your tools and data, and deliver a finished result, with human approval where it matters.

Book a free call See the samples

What is agentic AI?

Agentic AI is software built around AI models that can plan, use tools and take multi-step actions toward a goal, rather than only replying to prompts. Kaynat Umar develops agents that research leads, update CRMs, triage inboxes and run back-office tasks within defined guardrails.

Sample work

Run the agents. Watch every step.

Press “Run the agent” to replay a real-style run: each thought, tool call, result and approval appears as it happens.

Text agent · replay

Lead Research & Qualification Agent

Marketing agency · runs on n8n + Claude

When a new lead fills in the form, this agent researches the company, checks fit against your criteria, scores it, drafts a personal first reply and routes hot leads to the owner. A person approves every email before it is sent.

InputNew form submission: Daniel Reyes, Operations Lead at Northwind Dental Group (northwinddental.com). Message: “We have 4 clinics and miss a lot of calls after 5pm. Looking at AI options.”

10 steps · ~12 s

Agent tracethink → tool → observe → act

Press “Run the agent” to replay this run step by step.

Text agent · replay

Inbox Triage Agent

Founder inbox · runs on n8n + GPT/Gemini + Gmail API

Every morning it reads the overnight inbox, sorts each email by urgency and type, drafts replies in your tone from approved answers, and prepares a 30-second digest. Nothing is sent without your click.

InputOvernight inbox: 5 new emails (a client invoice query, a supplier price rise, a partnership pitch, a newsletter, a booking change request).

8 steps · ~12 s

Agent tracethink → tool → observe → act

Press “Run the agent” to replay this run step by step.

Voice agent · real recording

Voice Booking & Support Agent

Press play. The transcript follows the call, and on the right you see the tools the agent uses at the moment it uses them: order lookup, SMS, CRM.

AI receptionistGood afternoon, Lumière Skin Studio, this is Luma. How can I help?

CallerHi, I ordered the vitamin C serum last week and it hasn’t arrived yet.

AI receptionistI’m sorry about that, let me check for you. I can see your order left our studio on Tuesday, and it’s out for delivery today. I’ll text you the tracking link right now.

CallerThat’s perfect, thank you.

AI receptionistYou’re very welcome. Is there anything else I can help with today?

Live tool callssynced to the audio

Press play. Tool calls appear here at the moment the agent makes them.

Open the AI agent demos page

More proof

Keep exploring the live work.

Live sample

Why it matters

A chatbot answers a question. An agent finishes a job: it receives a goal, decides the steps, calls the tools it needs (CRM, calendar, email, databases, web), checks its own work and reports back. That shift is what turns AI from a novelty into operational capacity.

Every agent is built with boundaries. Permissions are least-privilege, risky actions need approval, every step is logged, and failure paths are designed up front. The result is autonomy you can trust and audit.

What it is	Autonomous AI agents with tool access
Typical delivery	2 to 6 weeks depending on scope
Control	Approvals, permissions, audit logs
Models	OpenAI, Gemini, Claude, chosen per task
Best for	Research, lead handling, back-office tasks
Support	Monitoring and tuning included

What’s included

Everything it needs to actually work.

Multi-step reasoning and planning

Agents decompose goals into verifiable steps and adapt when a step fails.

Tool and API integration

Direct connections to your CRM, inbox, calendar, databases and internal systems.

MCP-based architecture

Model Context Protocol connectors keep tools standardised and models swappable.

Guardrails and approvals

Defined permissions, spending limits and human-in-the-loop checkpoints for sensitive actions.

Memory and context

Agents retain the business context they need, scoped to what each task requires.

Observability

Logs, traces and cost tracking show what the agent did, why, and what it cost.

Process

How we build it

01Pick the right job

We choose a repeatable, well-bounded task with clear success criteria and a safe failure mode.

02Design the agent

Goals, tools, permissions, memory and approval points are mapped before any code.

03Build and evaluate

The agent is tested against realistic cases and edge cases, with measured accuracy.

04Deploy with monitoring

Go-live with logging, alerts and a clear rollback, then improve from real runs.

Best for

Real Estate Marketing Agencies SaaS Finance Law Firms

Typical tools

OpenAIClaudeGoogle GeminiModel Context Protocol (MCP)N8NSupabaseSlackGoogle Workspace

FAQ

Questions about agentic ai development

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

A chatbot responds to messages. An AI agent is given a goal, plans the steps, uses tools such as your CRM or calendar and completes the task, reporting back when it is done or when it needs approval.

Is agentic AI safe to run on my business data?

It can be when designed properly: least-privilege access, approval gates for risky actions, encrypted credentials and full logging. Safety is designed in from the start, not added later.

What tasks suit an AI agent?

Repeatable, well-defined work with clear success criteria: lead research and enrichment, inbox triage, report preparation, data entry across systems and follow-up drafting.

Which AI models do you use?

OpenAI, Google Gemini and Anthropic Claude models, selected per task for quality, speed and cost. The architecture keeps models swappable.

Your turn

Let’s see if Agentic AI Development fits your business.

A free 20-minute call. You’ll get a straight answer on whether it’s worth doing, what it would take and a fixed price.

Book a free 20-minute call Message on WhatsAppFree audit · fixed-price quote · no obligation