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Agents — how we build them

Not a chatbot bolted onto FAQs. A system built to run.

Every agent we ship is built on Skills, engineered context, and deployed on Azure AI Foundry — the same process behind everything already running in our portfolio.

Same five stages. Completely different work.

Traditional automation and agentic workflows both move through Idea → Design → Build → Measure → Improve. What happens inside each stage is where they split — and where the actual engineering work is.

💡

Idea

Traditional automation

Define a strict, multi-step rulebook for a computer to follow exactly.

Agentic workflow

Define a clear, high-level goal and decide what tools the AI needs to reach it.

🎨

Design

Traditional automation

Draw fixed flowcharts, edge-case branches, and rigid data-mapping rules.

Agentic workflow

Establish system prompts, tool boundaries, and evaluation guardrails for the AI.

🛠️

Build

Traditional automation

Code hardcoded loops, conditional statements (if/else), and API integrations.

Agentic workflow

Connect an LLM to tool APIs and write instructions on how to reason through the task.

📈

Measure

Traditional automation

Track system uptime, execution speed, and whether the code crashed.

Agentic workflow

Track token usage, accuracy, tool-selection correctness, and response quality.

🔄

Improve

Traditional automation

Rewrite hardcoded rules and update source code to handle new edge cases.

Agentic workflow

Refine instructions, optimize tool schemas, or add few-shot examples for the model.

How we build — 01

Skills, not one giant prompt

Agents draw on modular Skills — self-contained instructions pulled in only when a task needs them. Easier to test and extend than one sprawling prompt that degrades as scope grows.

How we build — 02

Context engineering

Instructions come from deciding exactly what information, tools, and constraints the model sees, and when. A good agent isn't a clever sentence — it's the right context arriving at the right moment.

How we build — 03

Deployed on Azure AI Foundry

Every agent ships to Azure AI Foundry — run, monitored, and scaled on the same Azure-first infrastructure as everything else we operate.

What's already running

Eight agents live in our own portfolio today — proof of the process above, not a pitch for it.

Create & publish5 agents

Reels on Autopilot

Faceless short-form video, script to ready-to-post MP4, no filming or editing.

Case study →

Automatic Video + Social Publishing

Faceless video creation and publishing across Facebook and YouTube, with LinkedIn/X queued automatically.

Automated Social Post Generator

Scheduled posts generated from a configured niche and published to Facebook with no manual step.

Excel → Social Automation

Turns a content spreadsheet into a running Instagram + Facebook publishing workflow.

Plan1 agent

PostReady

30-day content plan — hooks, captions, image prompts, hashtags, and reel scripts — delivered in Excel/PDF.

Capture & respond2 agents

AI Chatbot

Answers customer questions from a defined knowledge base and routes serious prospects to a meeting.

Website Form → Google Sheet

Every enquiry lands organized in a sheet automatically — no manual copy-paste from the inbox.

Knowledge systems1 system

AI Prompt Vault

200+ prompts engineered and tested across GPT-4o, Gemini, and Claude.

Case study →

Have a recurring task in mind?

Start with an Agent Audit — a short session to map whether it's worth automating first.

Book an Agent Audit