Agentic Automation in Financial Services: Why Orchestration Is the Real Challenge
Agentic AI—systems where autonomous agents handle tasks—has generated huge excitement. But the biggest obstacle isn’t what most people expect. It’s not about reasoning power, speed, or access to tools. The real bottleneck is orchestration: how we coordinate multiple agents to work together effectively.
What’s Wrong with Current Approaches?
Most teams use simple strategies like:
- Serial or parallel execution: Agents run one after another or all at once.
- Round-robin routing: Tasks are distributed evenly, as if agents were identical.
These methods assume agents are interchangeable, like identical parts in a machine. But that’s not true. Agents vary widely:
- Some excel at deep analysis. Some at research.
- Others are great planners.
- Some shine in creative problem-solving.
- Others can create graphics, images and charts that leads to the creation of a final report / outcome.
Treating them all the same wastes their unique strengths.
Another common approach is “named agents per job type”—assigning specific agents to specific roles. This works briefly, but it’s fragile. When business needs change or new tasks appear, the system breaks. It’s like an old, rigid content management system that quickly becomes outdated.
A Smarter Way Forward
A promising new idea is magentic orchestration1. Here’s how it works:
- Each agent shares a short profile of its strengths—an expertise vector.
- Each task is described by what it needs—an intent vector.
- A matching system pairs tasks with the best-suited agent.
How Expertise Vectors and Intent Vectors Work
A vector, in simple terms, is just an organized list of numbers that represent something. Think of it like a short summary or a “profile” made of numbers.
For example:
- If you describe a person by height, weight, and age, you could write it as [180, 75, 30]. That’s a vector.
- In AI, an expertise vector is a list of numbers that represent what an agent is good at.
- An intent vector is a list of numbers that represent what a task needs.
The system compares these lists to find the best match—kind of like comparing two sets of skills.
Instead of hardcoding workflows or assigning tasks by name, the system lets capability attract relevance. It’s more like semantic matching than mechanical scheduling.
This approach flips the question from:
- “Which agent should I call next?” to
- “Which agent wants this work because it’s best at it?”
Why does this matter? Because it adapts automatically:
- As agents learn new skills.
- As new agents join.
- As tasks become more complex.
No manual rewiring. No brittle logic trees. The system evolves naturally.
Think of it like a music playlist system
- Each agent is like a music track with its own genre, tempo, and mood.
- Each task is like a listener’s request: “I want something upbeat and energetic.”
- Instead of playing tracks in order or assigning fixed playlists, the system looks at the song’s characteristics (expertise vector) and the listener’s preference (intent vector) and picks the best match.
This way, the playlist adapts dynamically:
- New songs (agents) can join anytime.
- Listener tastes (business needs) can change.
- The system always finds the most relevant track without manual sorting.
Why Dynamic Orchestration Matters for Financial Services
Agentic automation won’t reach its full potential until orchestration becomes dynamic and adaptive. The future isn’t about rigid pipelines—it’s about systems that understand diversity in agent skills and match them intelligently to tasks. In short, progress may come from thinking less like computer scientists and more like musical conductors.
Learn more about Magentic Orchestration
1 https://learn.microsoft.com/en-us/semantic-kernel/frameworks/agent/agent-orchestration/magentic