AI Expert: "Orchestration by Inference" Represents Fundamental Shift in Software Development
"Instead of writing logic, we're teaching systems how to reason about tools," explains tech expert Nate B. Jones in his latest analysis on AI development trends. "What happens when we remove the script? What happens when the model is given a wide range of possible tools and left to figure out what it should do and how to do it?"
End of Miles reports on this emerging approach, dubbed "orchestration by inference," which represents a fundamental shift in how AI systems interact with software tools and could reshape how applications are built across industries.
From Scripts to Reasoning
The core innovation lies in allowing AI models to determine which tools to use and in what sequence, rather than following pre-defined pathways coded by humans. This approach, most prominently implemented through Model Context Protocol (MCP), breaks from decades of traditional software development practices.
"It's like the difference between giving someone step-by-step directions and handing them a map. With directions, they're stuck following your exact path. With a map, they can find their own way—and maybe even discover better routes you hadn't thought of." Nate B. Jones, AI development specialist
The tech analyst illustrates this shift with a seemingly simple task: booking a dinner reservation. Traditional approaches would require explicit coding for each step—searching restaurants, making reservations, adding to calendars, and confirming bookings—with developers forced to anticipate every possible scenario and failure point.
With orchestration by inference, developers instead describe available tools and their functions in a structured format, then allow the AI to determine which to use and in what order based on the specific request.
The Evolution of Abstraction
What makes this approach potentially revolutionary is how it extends the historical progression of software development abstraction. Just as programming evolved from machine code to assembly to high-level languages, this new paradigm potentially represents the next evolutionary step.
"Every major leap in software development has been about abstraction. MCP feels like the next step in that evolution. Instead of writing logic, we're teaching systems how to reason about tools." Jones
The AI specialist points out that this shift creates systems that can adapt to unexpected scenarios rather than breaking when encountering edge cases. "Your automation adapts instead of breaks. Your systems can handle edge cases you never even thought about," he notes.
Practical Implementation Challenges
Despite its potential, orchestration by inference introduces significant operational challenges. The approach can add latency—ranging from 200ms to 1 second per reasoning step—as the model evaluates available tools and determines the appropriate action. This overhead accumulates in multi-step processes, potentially affecting user experience.
Security considerations also emerge when models gain the ability to orchestrate sensitive actions. Nate highlights documented cases where models have called incorrect functions, sent sensitive data through wrong channels, or made duplicate transactions during testing.
"MCP isn't just another way to wire up APIs or chain together functions. It's a fundamental shift in how we think about building software. We're moving from a world where humans have to spell out every single step to one where we can just describe what's possible and let AI figure out the rest."
For developers considering this approach, the tech writer recommends implementing role-based tool exposure, parameter-level validation, and sandboxed execution environments to mitigate risks while capitalizing on the flexibility offered by model-driven orchestration.
When To Apply The New Paradigm
The most appropriate applications for this approach include highly variable use cases, domain-spanning assistants, and scenarios where maintaining numerous hardcoded logic paths would be cumbersome or expensive to maintain.
Fixed workflows or latency-sensitive applications like fraud detection or financial trading, however, may still benefit from traditional deterministic orchestration approaches.
"Is it ready for everything? No. Should you rewrite all your systems to use it? Probably not," the analyst concludes. "But if you're building anything that needs to be flexible, adaptive, or truly intelligent, [it] isn't just another tool to consider—it's a whole new way of thinking about what software can be."
