What starts as smart automation can turn into chaotic fragmented logic the moment you accumulate enough agents doing slightly different things in slightly different ways without a proper orchestration.
We worked with a CTO who had this exact situation brewing inside their finance ops stack.
A few tools were running:
🔹 One agent was handling invoice parsing.
🔹 Another flagged irregularities in cash flow trends.
🔹 A third was generating weekly reports for leadership team.
Each tool was chosen for a reason and worked great on its own.
But time and time again, combining them together produced an unpredictable result.
🔻 Approvals were getting skipped.
🔻 Flags popped up that didn’t make sense.
🔻 People weren’t sure whether they were dealing with a bug, bad data, or just another over-eager assistant.
What broke wasn’t the models. It was the orchestration and interoperability layer.
💡 That’s where we got involved.
We treat finance ops not as back-office admin, but as a system where timing, traceability, and trust all intersect. So when multiple agents show up it’s easy for the whole thing to drift.
What we did was fine-tune the Model Context Protocol (MCP) to fit the actual shape of the entire system. That meant balancing autonomy of each agent, making sure any shift in state could be tracked across the stack. A few simple structures that made everything less fragile:
🔹 Normalized logs so behavior could be traced back without guesswork
🔹 Guardrails around who or what could trigger changes to different types of data
🔹 And a changelog that made agent decisions transparent
And the CTO was no longer wasting hours triangulating errors that should’ve never existed.
It’s never been easier to integrate AI. The hard part is knowing where to draw the lines and how to ensure the system respects them.
If you’re seeing coordination slip — between agents, teams, or systems — and you want clarity without the noise, let’s have a talk.
