AI Orchestration Platforms

Overview

Most companies end up with a pile of disconnected AI tools. A chatbot here, a summarizer there, an automation script somewhere else, each with its own memory, its own prompt logic, and no shared state between any of them. Orchestration turns that into one coordinated system instead: a shared architecture where agents and tools hand off work, share memory, and stay observable from a single place, rather than a stack of point solutions nobody fully understands anymore.

Pricing

Implementation gets scoped after the architecture phase, once the actual integration surface is actually known rather than guessed at up front.

How I Build This

Agents and tools share a memory and state layer instead of reasoning from isolated, disconnected context. Model routing stays provider-agnostic, so the orchestration layer isn't locked to one LLM vendor and models get swapped or mixed based on cost, latency and capability per task. Hand-offs between agents and tools follow explicit protocols rather than implicit prompt-chaining that breaks the moment one step changes underneath it. And observability shows up from day one: every hand-off and decision gets logged and stays inspectable, instead of turning into a black box you just have to trust.

This is the same architectural discipline behind SharapovLab Trading Lab, where a swarm of agents, a debate pipeline and a risk engine all coordinate through one shared state layer.

Who This Is For

Companies with several AI tools already in production, or close to it, who are hitting the ceiling of point solutions that don't talk to each other, and need one architecture underneath instead of yet another disconnected tool.

FAQ