We’re exploring how autonomous AI agents could work for us during and after a BPM migration. The idea is compelling—multiple AI agents handling different parts of an end-to-end process, making decisions, coordinating handoffs. But I’m concerned about governance and what happens when things go wrong.
Specifically, I’m thinking about scenarios where multiple agents are working on the same business process across different departments. Sales hands off to operations, operations coordinates with fulfillment, fulfillment tracks with finance. If each step is handled by an autonomous agent, who’s actually checking that the process is running correctly? Who’s responsible when it isn’t?
I’m worried about a few things:
If agents are making autonomous decisions, how do you audit what happened and why? Do you end up with a governance mess where it’s unclear which agent made which choice?
What happens when an agent encounters a scenario it wasn’t trained on or designed for? Does it escalate, or does it guess and create problems downstream?
Can you actually simulate what would happen with agents running your current end-to-end processes, or does the simulation miss too much variation to be useful for planning?
When agents hand off work between departments, how do you maintain consistency in how data is formatted, validated, or interpreted?
I want to use agents to accelerate our migration planning, but I need to understand the real constraints on what they can coordinate without exploding complexity.
We ran a pilot with autonomous agents coordinating between three departments, and I learned a lot about what works and what requires explicit rules.
Agents handle repetitive, well-defined handoffs beautifully. Our accounts team had an agent that decided which deals to escalate based on contract terms, then passed them to legal. That worked because the decision criteria were explicit and the handoff format was standard. Where we hit friction was when agents tried to interpret ambiguous requests or when the downstream department expected different data than the agent was passing.
Governance is the real challenge. We had to build explicit logging and approval workflows. Agents don’t get complete autonomy—they get guardrails. On routine decisions, they act. On anything unusual or high-value, they flag for human review. It’s not fully autonomous, but it’s still faster than manual review of everything.
For simulation, agents showed us where our current process had implicit knowledge or informal rules that we never documented. When we tried to get agents to simulate the current workflows, they exposed gaps in our process definition. That was actually valuable for migration planning because it forced us to articulate how things really work, not how we think they work.
My advice: start with well-defined, lower-stakes processes. Get the coordination patterns clear. Then expand. Trying to have agents coordinate complex, poorly documented processes is going to create chaos.
Autonomous agents are good at specific things and bad at others. Where we saw real value was when the handoff rules were explicit and predictable. Finance to accounting, for example—the rules were clear, the data format was standardized, and agents handled it well.
What broke was cross-functional work where the interpretation depended on context. Sales to operations is easy if every order follows the same path. But if 20% of orders need custom handling, agents either over-escalate everything (defeating the purpose) or mishandle the edge cases.
On governance, yes, you absolutely need audit trails and escalation rules. We built a system where agents make routine decisions and flag exceptions. The escalation isn’t a bug—it’s part of the design. You’re not replacing decision-makers, you’re automating the obvious ones and surfacing the complex ones faster.
Simulating current processes with agents? It works, but only for processes you’ve already documented well. If your current process is “John does it based on experience,” agents will expose that you don’t actually know what John is doing. Sometimes that’s a good realization during migration planning.
The coordination part is actually manageable if you build governance into the agent design from the start. I’ve seen teams fail because they tried to give agents complete autonomy and then retrofit governance. That’s backwards.
What works: agents operate within explicit decision rules, they log everything they do, and they have clear escalation paths. You’re not making agents autonomous in the sense of unsupervised. You’re automating routine decisions and surfacing exceptions.
For cross-department coordination, the real issue is data consistency. If sales formats customer IDs one way and operations expects another, agents will pass bad data. Governance means standardizing the format and validation before agents touch it.
Simulation for migration planning is powerful because it shows you where your current process has undocumented complexity. When an agent can’t decide, that means you haven’t formalized the rule yet. During migration, you either formalize it or accept that humans still need to handle that case.
Start small—one well-defined handoff between two systems. Get that running, audit it, understand the governance needs. Then expand. Trying to coordinate a full end-to-end process with autonomous agents immediately is going to fail.
Agents work for routine decisions, but you need explicit rules & escalation paths. Governance isn’t optional—it’s part of the design. Build it in from day one, not after.
This is exactly what I’ve seen work with Latenode’s autonomous AI teams. Agents don’t need to be fully autonomous to be useful—they just need to handle the routine decisions and escalate the complex ones.
We built a multi-agent system where each agent owned a specific decision point. Sales agent: qualify leads. Operations agent: schedule fulfillment. Finance agent: validate pricing. Each knows its job, and they coordinate through explicit handoff points. The magic is the governance is built in—logging, escalation rules, approval workflows.
For cross-department coordination, Latenode lets you model the orchestration visually, so you see exactly how agents interact, what data flows between them, and where it might break. That visibility is crucial for meeting governance requirements.
During evaluation, we simulated our current end-to-end process with agents and discovered we didn’t actually know what our current process was. John in operations was making decisions based on undocumented rules. Agents forced us to document it, which helped us see what we could automate and what still needed human judgment.
The key: start with simulation and governance design. Use agents to pressure-test your process definition, then scale the automation once you understand what needs it.