The Business Challenge
The business situation the organisation faced before the project began.
Early-stage founders and small teams juggle sales, marketing, support, research, finance and hiring with little headcount. Single-purpose AI tools each cover one function, do not share context, and still leave a person to coordinate everything and track business health manually.
Why AI Was the Right Solution
Why AI was the right approach — and what alternatives were considered.
Business tasks are open-ended and change daily, so fixed scripts or rules cannot cover them. Hiring a person for every function is not realistic for an early-stage team. LLM-based agents can interpret goals written in plain language, plan the steps and adapt, and splitting the work across specialist agents keeps each one focused and easier to improve.
How the Solution Was Delivered
Discovery, design, development, testing and rollout — the journey, not the tooling.
- 1Defined the six business functions a small company depends on and the responsibilities of each.
- 2Designed a CEO agent to break goals into tasks and route them to the right specialist.
- 3Built the specialist agents for sales, marketing, support, research, finance and hiring.
- 4Added shared memory in Redis and PostgreSQL so agents keep context across tasks.
- 5Built a digital-twin dashboard tracking MRR, revenue, burn rate and runway.
- 6Containerised the system with Docker and deployed it on AWS EC2.
- 7Tested it with 4-5 users and refined task routing based on their feedback.
Key Technical & Architecture Decisions
Architecture, model selection, workflow and trade-offs.
Hierarchical design: a CEO agent plans and delegates, specialists execute, which keeps responsibilities clear.
Shared memory split between Redis for fast working context and PostgreSQL for durable records.
FastAPI for the agent backend and Next.js for the dashboard, keeping the API and UI cleanly separated.
Docker on AWS EC2 for a simple, reproducible deployment.
Challenges & How They Were Solved
Obstacles hit along the way and how they were overcome.
Keeping context consistent between agents: solved with a shared memory layer so each agent sees the relevant history.
Routing tasks to the right agent: the CEO agent was refined so goals map cleanly to a single owner.
Making the output trustworthy: the dashboard gives a clear, numeric view of business health alongside agent activity.
User Adoption & Change Management
How users responded — training, change management and feedback loops.
The system was tested by 4-5 users. The most useful feedback was about clarity: users needed to see what each agent was doing and why. Making delegation visible and grounding it in the dashboard numbers helped users trust the system.
Business Outcomes & Impact
The measurable outcomes from the underlying AI Deployment — with the story behind them.
7 coordinated agents covering 6 business functions; deployed on AWS EC2 and tested by 4-5 users.
The story behind the numbers
A founder can state one goal and see it broken down and worked on across six functions, instead of coordinating separate tools by hand. The digital-twin dashboard puts MRR, revenue, burn rate and runway in one place, so decisions are made against current business health rather than scattered spreadsheets. Testing with 4-5 users showed the delegation model works end to end.
Lessons Learned
What surprised the team, what worked well, and what would be done differently.
- A planner agent with clear specialist roles keeps multi-agent systems manageable.
- Shared memory is essential for consistency across agents.
- Visibility into agent actions builds user trust.
- Simple, reproducible deployment speeds up iteration.
Future Opportunities
Where this solution could go next.
Human approval steps for higher-risk actions such as spending or hiring decisions.
Integrations with real CRMs, accounting and support tools.
More specialist agents, such as legal and operations.
Evaluation tooling to measure agent quality over time.
Professional Reflections
Guidance for another builder tackling a similar problem.
Start with a clear division of responsibility between agents; most problems in multi-agent systems come from blurred ownership. Invest in shared memory early, and give users visibility into what agents are doing before trying to automate more.