AI Agent Orchestration Implementation - Day 1
We've just started an ambitious project with a technology client, documenting the journey in real time as we build and deploy an AI agent orchestration system.
The Client Context
Our client is a small technology business in a highly competitive market. They’re developing multiple products while serving clients and pursuing new opportunities. Their challenge is universal: staff have too much to do and not enough time to do it. But as a business, should they increase headcount or invest in technology tools to ease the burden (and risk adding to it)?
In working with us, the client has defined three primary goals:
Increase their productivity
Control costs, and
Establish the technology foundations and ‘AI culture’ needed for long-term competitive advantage.
They came to us with a clear problem. Despite having talented people and promising products, they were struggling to finish what they started. Projects would launch with energy and enthusiasm, then stall halfway through as attention shifted to the next urgent priority. Sound familiar?
The issue wasn't capability or commitment, but capacity and access to experts and specialists across critical functions. Hiring specialists wasn't financially viable, but continuing without expertise was limiting their growth.
Our Solution: An AI Agent Army
Instead of recommending traditional consulting or expensive hires, we proposed building an AI-powered ‘army of agents’ to augment the current, small workforce, with 27 specialized agents in a coordinated hierarchy.
The architecture mirrors a real company: a master orchestration agent coordinates all functions, with managing director agents, personal productivity agents, and functional specialists covering areas like marketing, legal, finance, regulatory technology, cybersecurity, and ESG compliance.
What makes this useful is the integration work. These agents aren't just chatbots; they're embedded in workflows, read and write to the task management system, understand goal hierarchies, and track completion. One agent even serves as a personal coach for the leadership team.
Where We Are Today
We've completed the foundational build: 27 agents with defined roles and reporting structures, and operational bidirectional task management integration. Our four-tier goal system ranges from tactical to long-term strategic goals.
Mobile access is live via a Telegram bot, allowing the team to interact with the agent network anywhere. A monitoring dashboard provides insights into agent activity, scheduled jobs, and performance.
Our Approach: Daily Iteration and Refinement
The foundation is built, but this is where the real work begins. Starting tomorrow, we will enter a phase of daily iteration based on actual usage and client feedback. This isn't a fixed system we're deploying and walking away from. It is designed from the outset to evolve in response to how the agents are actually used by staff to improve their daily productivity.
Every interaction the client has with the agent system generates insights. Which agents are they consulting most frequently? Where are the friction points in the workflow? What types of tasks do the agents handle well, and where does human judgment prove irreplaceable? We're capturing all of this, and we're using it to refine the system daily.
This approach reflects a fundamental truth about AI implementation: the initial build matters, but the real value emerges through continuous refinement driven by real-world use. Theory and frameworks provide the starting point. Daily iteration based on actual client feedback creates a system that delivers genuine business impact.
What Makes This Different
We’ve seen many companies bolt ChatGPT onto processes and call it an AI transformation. Our difference lies in deep integration and organisational design thinking.
These agents maintain context for ongoing projects, understand goal hierarchies, ensure alignment with objectives, and schedule tasks based on deadlines and dependencies. Our system mirrors proven organisational structures, applying hierarchy, specialisation, and clear reporting to AI agents as with human teams. Perhaps most importantly, we are working with existing staff to deliver this change and focusing on an iterative rather than a big-bang approach.
We're building this with long-term operations in mind. This isn't a proof-of-concept that gets abandoned after the initial excitement. We've designed for maintenance, monitoring, continuous improvement, and scalable growth as the client's needs evolve.
Why Document This Publicly
We're sharing this journey for three reasons: transparency builds trust by showing both challenges and successes; it demonstrates our capability to those considering AI implementation; and it forces us to document our decisions, improving our own work.
Tomorrow's update will dig into the goal management framework in detail, report on the first 24 hours of using this AI Agent system, and share initial observations about where the agents are proving most valuable.
If you're running a small technology business competing against larger players, or if you're struggling with the universal challenge of starting more than you finish, this series might be worth following. We're documenting exactly how AI agent orchestration can level the playing field.
Watch out for tomorrow’s update: Goal Management Framework and First 24-Hour Results