Agentic BPM fuses BPMN workflows with AI agents — LLMs reasoning over tools, data, in orchestrated flows. in-concert makes it real by keeping data external.

Why keep process data outside the BPM engine?

Prevents bloat, security leaks, integration hell. Engine orchestrates; your systems own data — scalable, flexible for AI fuzz.

How does in-concert handle LLM tool calls?

Via handlers: BPMN tags toolId, your code maps context to schema, invokes, maps back. Full control, async-friendly.

🤖 AI & Machine Learning

Ditching Data in BPM Engines: The Radical Path to Agentic Workflows

Everyone figured BPM engines would hoard your data like dragons on gold. in-concert says nope — and that bare-bones choice ignites agentic BPM like a rocket booster.

Open Source Beat Apr 11, 2026 4 min read

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Illustration of a lightweight BPM engine baton-conducting external data systems and AI agents

⚡ Key Takeaways

in-concert's no-data engine liberates BPM from legacy bloat, enabling true agentic AI workflows. 𝕏
LLMs shine as mappers between domain data and tools, keeping BPMN clean and iterable. 𝕏
This shift predicts agentic BPM dominating enterprise AI by 2026, echoing database revolutions. 𝕏

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#LLM agents #LLM integration #LLM-tools #agentic-bpm #bpmn-engine #bpmn-engines #in-concert

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Originally reported by Dev.to

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