I’m building an agent-based AI orchestration platform whose sole mission is to boost operational efficiency across the organisation. The system must coordinate multiple models and services behind a single interface, then surface clear, actionable insights to the team. What matters most to me is that the platform delivers on three fronts: • Task automation – agents should reliably pick up repetitive, rules-driven work and complete it end-to-end through API calls or RPA hooks. • Data analysis & reporting – the same agent layer must digest raw logs, databases, and third-party feeds, then produce dashboards or scheduled reports that decision-makers can trust. • Resource management – I want real-time visibility into how people, time, and compute are being used, with recommendations (or automated actions) that trim waste. You’re free to choose the underlying stack—LangChain, Haystack, or a custom micro-service approach—as long as each module is containerised, well-documented, and can be deployed on our Kubernetes cluster. Expect to integrate at least one large-language model (OpenAI, Anthropic, or open-source equivalent) plus a vector store for memory and retrieval. For hand-off, I’ll look for: • A Git repo with clean, commented code • Infrastructure-as-code for repeatable deployment • A short video walkthrough showing the agents in action and the key performance metrics improving If the above reads like your kind of build, let’s talk timelines and milestones so we can get the first agents live quickly.