we’re preparing for an open-source BPM migration, and we’re thinking about using autonomous AI agents to run a simulated migration before we commit to the real thing. the idea appeals to me—basically have agents model the migration steps, flag bottlenecks, and run what-if scenarios without touching production.
but i’m trying to understand what a simulation like that actually surfaces. like, obviously you can model timelines and task dependencies. but what about the stuff that only emerges when you run processes? integration edge cases? data quality issues that don’t show up in dry runs? teams making decisions in ways that violate your assumptions?
i’ve been thinking through this and i suspect the real value isn’t in the simulation answering everything perfectly, but in discovering the questions you didn’t know to ask. things like:
which data transformations are actually complex vs. just tedious
where your current system has implicit logic that nobody documented
which integrations will actually timeout if they run in parallel
how your team will actually handle exceptions (vs. how the process docs say they will)
but maybe i’m overthinking it. maybe simulations with autonomous agents are best used for cost estimation and timeline modeling, and they’re not going to catch the weird human-decision-making stuff anyway.
if anyone has run a simulated migration with autonomous agents or workflow automation, what surprised you about what got revealed? and what still had to be validated in a real test environment anyway?
we ran a simulation-based migration study for a Camunda to open-source transition and the most useful insight had nothing to do with the happy path. it was in the exception handling.
our simulation had agents model each migration task: data mapping, testing, integration validation. when we ran it with realistic failure rates (some data transformations fail, some integrations timeout occasionally), we discovered that our parallel testing strategy had a cascade failure mode. like, if three workflows hit integration issues at once, the testing team’s validation queue would back up and push us past our cutover window.
that was something we’d never have caught in theory. we weren’t thinking about concurrency constraints during migration because our planning assumed sequential phases. the simulation exposed that and we restructured the whole migration to phase workflows differently.
the other thing it surfaced: documentation gaps. when agents tried to execute tasks that relied on documented business logic, they hit cases where the documentation was incomplete or contradicted what actually happens. that told us we needed actual process walkthroughs with domain experts before migration started.
so yes, simulations catch the structural stuff you’d miss. but they also force validation.
the real value of simulation is that it compresses feedback cycles. instead of discovering issues during actual migration, you discover them in a safe environment first.
what got exposed in our simulation: data quality issues we hadn’t anticipated. when agents tried to map and validate data, they flagged malformed records, missing relationships, and inconsistent naming conventions. We were planning to clean data during migration, but simulation showed us we needed to clean it before migration started because the volume was huge.
also, skill gaps. agents modeled the tasks, but when we traced through what tasks required manual human judgment, we realized we didn’t have enough people with the right expertise to handle the parallel workload. that led us to bring in contractors earlier.
Simulation with autonomous agents is most useful for identifying structural dependencies and bottlenecks. It’s less useful for predicting human behavior and decision-making patterns.
What actually gets exposed: task sequencing constraints, integration complexity hotspots, data quality issues, and capacity planning gaps. What often still needs real validation: accuracy of business logic in transformed workflows, user acceptance of system behavior changes, and team handling of edge cases.
Treat simulation as a planning accelerator, not a substitute for UAT. The combination is powerful—simulation gives you a risk map, UAT validates that your risk mitigation actually works.
Simulations expose task dependencies, data issues, capacity gaps. Doesn’t predict human decisions or edge cases. Use it to plan, then validate in real testing.
We built autonomous AI teams to simulate a major workflow migration and the simulation approach genuinely changed how we planned.
What got exposed: not just technical bottlenecks, but process assumption gaps. The agents modeled data mappings, integrations, and validation sequences. When they ran through scenarios with realistic failure rates and concurrency, they flagged this: our testing strategy assumed sequential validation, but if multiple workflows migrated in parallel, our validation team would be bottlenecked.
That forced us to restructure the entire migration timeline. Or for instance, when agents tried to execute business logic based on our documented processes, they hit cases where documentation was incomplete. That told us we needed expert walkthroughs before the actual migration.
Most importantly, the simulation became a shared language between technical teams and business stakeholders. Engineers could show finance the cost impact of different phasing strategies. Business could see where their manual decision-making was a constraint.
The key: use Latenode’s autonomous team orchestration to model your actual workflow steps, let the agents run scenarios with realistic constraints and failures, and use that output to stress-test your migration plan before committing budget.
Simulation handles the structural planning; UAT still validates edge cases. Together they compress migration risk significantly.