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Agents

LlamaIndex Workflows + ReActAgent

Event-driven workflows with durable execution — context serialized at every step, human-in-the-loop runs that park at zero cost and resume on approval.

Durable by construction

Research workflows here (multi-step runs over the corpus — company benchmarks, cross-corpus briefs) are LlamaIndex Workflow subclasses, and their execution is durable: after every non-terminal step the workflow context is serialized (ctx.to_dict(JsonSerializer())) into a run store. A crash, deploy, or restart loses nothing — the run rebuilds from its last step and continues.

Steps are the natural checkpoint boundary: events in, events out, state in Context. Serialize at the seams and durability is a property of the design rather than a bolted-on retry layer.

Human-in-the-loop that costs nothing while it waits

When a workflow needs a person, it emits InputRequiredEvent and parks — the run sits in a waiting_human state consuming zero compute. A HumanResponseEvent resumes it exactly where it paused (ctx.wait_for_event). The interrupt happens before the sensitive action, not after: HITL as a guardrail on autonomy, not an afterthought. A demo workflow exercises the full pause → approve → resume cycle end to end in the runs console.

ReAct with real tools — and the lab

A ReActAgent runs over the platform's actual toolbox (the paper-triage tools, wrapped as LlamaIndex FunctionTools). Around it, the agents lab implements the major orchestration patterns from scratch — ReAct, LATS, ReWOO, Reflexion, LLM-Compiler, plan-and-execute, tree-of-thoughts, self-refine, debate, supervisor — each one runnable, not a diagram.

Testing agents deterministically

Live-LLM agents hide their trajectory, which makes them untestable. The knowledge-graph lane runs a scripted ReAct loop — same thought/action/observation structure, deterministic trajectory — so agent behavior is assertable in CI. Non-determinism is confined to the outermost layer and judged there (evals); everything inside is exact.

The same ideas appear in the LangGraph curriculum here — durable checkpointers, HITL interrupts, multi-agent supervision — grounded in a real 60+ subgraph production case study rather than toy examples; the Agents & Workflows guide walks that system end to end.

Numbers

  • Context serialized after every non-terminal step — a crash or deploy loses nothing.
  • A parked waiting_human run consumes zero compute until its HumanResponseEvent arrives.
  • Ten orchestration patterns implemented runnable-from-scratch in the agents lab.
  • The companion LangGraph case study spans a 60+ subgraph production system.