Step Execution Trace Log & Payload Inspector
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Workbench initialized. Default ReAct Tool-Calling topology loaded. Ready to run steps.
How do modern AI agent workflows coordinate reasoning steps, tool calling, and evaluation loops within directed execution graphs?
This interactive workbench visualizes AI agent workflows as directed graphs rendered with Cytoscape.js, spanning simple routing, retrieval-augmented generation (RAG), and interleaved ReAct architectures. In an operational agent graph, state is passed between decision nodes, external retrieval tools, and evaluation checkpoints. The simulated step execution demonstrates how iterative tool interactions generate observations that update the agent's internal reasoning state before emitting a final grounded answer.
The runtime metrics in this interface—including token counts, the fixed $0.000015 per token cost calculation, and latency numbers—are pre-scripted didactic estimates rather than live measurements from external LLM APIs. Additionally, adjusting the sidebar parameters (such as LLM Temperature or Max Reflection Loops) changes local input controls but does not alter the hardcoded step traces in the preset demonstration.
Select the 'W2: ReAct Loop' preset tab to load a 5-node reasoning and acting graph. Click 'Run Execution Step' once: the 'user_input' node highlights, adding 30 tokens and 40 ms of simulated latency to runtime telemetry. Click 'Run Execution Step' a second time: the graph activates 'react_reason' (Thought 1), advancing Tokens Consumed to 210 and Est. Cost to $0.0032. Continuing through steps 3 and 4 routes execution to 'tool_act' (Action) and 'observation' (Sink), completing one canonical ReAct cycle before delivering the final answer.
The ReAct framework coordinates large language models by interleaving reasoning traces ('thoughts') with task-specific actions such as querying external APIs or executing code in sandboxes. ReAct: Synergizing Reasoning and Acting in Language Models
By incorporating external observations back into the prompt context, ReAct allows the model to dynamically update action plans, verify intermediate outputs, and mitigate hallucination cascades common in pure chain-of-thought generation. ReAct: Synergizing Reasoning and Acting in Language Models
Structuring complex agent workflows as directed computational graphs enables explicit branching, state isolation, and self-correcting reflection loops. Instead of relying on an unstructured context window, supervisor agents and evaluator nodes can validate schema conformity or unit test passes before routing to a terminal output state.
Defines the foundational ReAct pattern of interleaving thought, action, and observation cycles for agentic decision-making and external environment interaction.
The saved architecture workbench includes authored curriculum traces. Its basic router preset has three steps with assigned token counts forty five, one hundred twenty and three hundred fifty, totaling five hundred fifteen. Their assigned latency values fifty, two hundred twenty and four hundred total six hundred seventy milliseconds. At half a pixel per assigned token, the bars measure twenty two point five, sixty and one hundred seventy five. These values are stored teaching fixtures rather than measured inference usage, benchmark timings or executed output. The graph can contain multiple output branches while this preset trace follows one predetermined response path. Each step adds its token count times zero point zero zero zero zero one five to the cost counter. For five hundred fifteen tokens, that produces zero point zero zero seven seven two five currency units. A fresh run after the final step resets tokens, cost and latency before replaying the first record. The context fill rounds total tokens divided by the configured context limit and caps at one hundred percent. A full bar is therefore a clipped display, not a demonstrated context-window failure. At one pixel per millisecond the first two assigned latency values measure fifty and two hundred twenty. These accounting rules illustrate accumulation rather than current provider pricing. The default week four trace contains seven entries. Its first three assigned token counts sixty five, four hundred eighty and three hundred twenty sum eight hundred sixty five. At half a pixel per token the bars measure thirty two point five, two hundred forty and one hundred sixty. Names such as web search, Python execution and verification are authored log messages, not evidence those tools actually ran. Export is designed to include complete preset steps, graph data and a telemetry snapshot rather than only steps already visited. In this offline check the default preset invokes Cytoscape before the control listeners are attached, so missing graph initialization prevents native stepping and export. That preserved limitation remains separate from actual playback of this local narrated explainer.