Multi-Agent Workflow Engineering

• Your process runs as a validated, versioned graph of specialist agents — parallel branches, sub-workflows, governed tool access, and declarative failure handling
• Deterministic, observable execution — supersteps you can watch advance, with a live event stream of every node's start, completion, and failure
• A human-in-the-loop seam in the engine itself — a consequential step escalates to a person, then the run acts on the decision
• A runtime you own and run — provider-agnostic at the LLM seam, not agents rented on a hyperscaler's platform

How it works

A real multi-agent workflow is not a chatbot in a loop. It is a graph: specialist agents, parallel branches that run at once, conditional routing between them, nested sub-workflows, and a join that waits for the right branches to finish before moving on. We engineer your process as exactly that graph, then run it on an orchestration engine we built and own.

The engine is bulk-synchronous-parallel — it advances in deterministic supersteps you can watch tick forward, runs independent agents in real parallel under a concurrency cap, and implements true join semantics (wait-for-all, wait-for-any, race). It prunes branches that the data makes irrelevant, nests sub-workflows to any depth, and emits a live event for every node as it starts, completes, or fails. This is the runtime the rest of the market only scripts on someone else's library.

We build it through the D2D Line: we FRAME the process and the decision each agent owns, DESIGN the graph and get your sign-off, BUILD it test-first, VERIFY it on a runnable execution, and SHIP it to you — the graph definition, the engine integration, and the tests.

The honest limit, stated plainly

The shipped engine is single-process and in-memory. It runs real parallel agents and real join semantics within one process; it is not horizontally scaled or distributed across machines. Distributed, multi-machine orchestration is research-stage, not something we ship today. For the overwhelming majority of business workflows the in-process engine is exactly the right tool — and we will tell you plainly if yours is the case that needs more, rather than overselling what we run.

What you own at the end

The workflow definition, the engine integration, and the test suite are yours — source and all. The engine is provider-agnostic at the LLM seam, so you are not renting your agents on a single hyperscaler's runtime. Your team can change the graph, add agents, and re-run it without us.

FAQ

What does "deterministic" buy us? Repeatability and observability. The same graph and inputs advance through the same supersteps, and you get a live trace of every node — so a run is something you can reason about and debug, not a black box.

Can a human approve a step mid-run? Yes — a human-in-the-loop seam is built into the engine, so a consequential step can escalate to a person and the run acts on their decision. (Approval inside a deeply nested sub-workflow is a boundary we will flag for your specific graph.)

How is it priced? Fixed-scope, fixed-fee per workflow engagement. The process and the graph are pinned in the FRAME phase, so the scope is bounded before any build starts.

• An owned bulk-synchronous-parallel orchestration engine — deterministic supersteps, And/Any join semantics, data-driven branch-pruning, recursive sub-workflow nesting, and inline human-in-the-loop escalation — backed by hundreds of automated tests across dozens of files.
• A type-safe, declarative agent-graph builder with three-color cycle detection, sub-workflow nesting, And/Or/race joins, and least-privilege tool governance per node — validated at build time, fully unit-tested.
• A runnable demo — a splitter fans out to parallel workers and an aggregator joins them across multiple supersteps — printing each round as it advances, against a real LLM provider.