Orchestration: The Missing Layer In Your AI Stack
This article dives into the missing orchestration layer which is holding back agents from behaving like truly intelligent systems.
Over the last two decades, technology has changed the way we answer questions. For nearly twenty years, Google reigned supreme. It gave us a seamless product: type in any query, and within a blink, you’d have a blue link to the best resource the world had published. It felt magical — humanity’s knowledge, instantly at your fingertips.
But recently, something new arrived. With ChatGPT and other large language models, we experienced the next version of that magic. No more scanning links. No more digging through articles. Instead, you could ask a question directly — and get an answer, crafted just for you.
At first, it felt like an evolutionary step. But the more we used it, the clearer it became: this wasn’t just faster search. This was something different. A step-change. Google gave us access to answers that already existed. ChatGPT gave us answers to questions so personal, contextual, and nuanced that no one had ever written them down before.
The first spark of intelligent personalization had arrived.
The Cracks AppearThe Cracks Appear
Of course, anyone who used these systems quickly noticed the limitations. The most infamous was hallucination — the tendency of models to produce answers that sounded plausible but were simply untrue.
The fix? Ground the model in real-world data. That’s why Retrieval-Augmented Generation (RAG) and data-connected systems became so important. With RAG, models could pull context from live data sources, documents, or APIs, and generate answers that were not only fluent but factual. Suddenly, the AI felt less like a confident improviser and more like a knowledgeable assistant.
But as impressive as this was, something was still missing. Because even with RAG, you only got answers.
And answers can only take you so far.
From Answering To DoingFrom Answering To Doing
That’s when the conversation shifted from LLMs to Agents.
If models could only answer questions, what if they could also take action? What if they could plan, decide, and execute — not just inform you about what should be done, but actually go and do it?
This is what agents promised. By generating function calls, they bridged natural language with software. A high-level instruction like “Create a customer support ticket for this complaint” could translate into precise steps: connect to HubSpot, call the API, log the ticket.
The implications were enormous: fewer developer hours hardcoding business logic, and workflows that could be generated dynamically. For the first time, it felt like we were close to assistants that didn’t just advise us, but truly worked alongside us.
But once again, reality intruded.
Building An Agent Is Harder Than It LooksBuilding An Agent Is Harder Than It Looks
On paper, an agent sounds magical. In practice, the complexity is staggering.
Take something simple: building an AI agent to add a survey feature to your app. What would it actually take?
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Writing new code — and running it safely in a sandbox
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Reading your existing codebase to fit the new feature in
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Pulling in business context like branding, policies, or app rules
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Coordinating workflows like deployment scripts or database updates
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Handling retries, errors, and failures along the way
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Remembering context between steps — and across entire sessions
That’s not just one step of reasoning. It’s a delicate dance between planning, executing, adapting, and coordinating. And it requires robust scaffolding: memory, error handling, workflow orchestration, context management.
Most frameworks give you tools and abstractions. But they leave you with the hardest part: wiring it all together.
And that’s when the question hits:
If AI is so intelligent, why are humans still stuck solving the plumbing?
The Missing PieceThe Missing Piece
The answer is Orchestration.
Orchestration is the connective tissue that makes agents truly intelligent systems.
Think of it this way: if LLMs are the brain, and tools are the organs, then orchestration is the nervous system. It coordinates everything, ensures signals move where they need to go, and allows the whole system to act coherently. Without a nervous system, the parts remain powerful but isolated. With it, they form a body.
Orchestration brings together LLMs, tools, storage, data sources, microservices, and workflows into a unified whole. It’s what gives agents memory, structure, and the ability to work together instead of operating in silos.
It turns fragments into function.
Why This Matters NowWhy This Matters Now
Look around today’s AI landscape and you’ll see incredible pieces: models that reason, APIs that act, databases that store, frameworks that abstract. Each is powerful on its own. But stitched together haphazardly, they’re brittle.
Orchestration changes that. It provides the system-level pathways for everything to interoperate smoothly, creating agents that don’t just seem intelligent in isolation, but that behave intelligently as part of larger systems.
This is what transforms AI from a collection of scripts and demos into something you can trust to run real workflows, power real products, and work alongside real teams.
The Role Of DextoThe Role Of Dexto
This is the gap Dexto fills.
On their own, today’s AI components remain siloed, fragile, and limited in what they can achieve together. Dexto provides the orchestration layer — the nervous system — that connects reasoning models, tools, storage, and data into one functioning system.
It’s what turns isolated capabilities into intelligent products.
It’s the missing piece.