Interactive lab · Published October 7, 2026

AI agent workflow simulator for parallel research

Plan a multi-agent research workflow and compare human-only work, one sequential AI agent, and parallel AI agents. Adjust task dependencies, agent speed, reliability, cost, research complexity, and human review to see the useful part of parallelism—and its hard limits.

Build a research workflow

1 · Choose the job

Construct the workflow

Start from a preset, remove work you do not need, and adjust the human-time estimate for each task.

Human estimate25 min
Human estimate70 min
Human estimate90 min
Human estimate80 min
Human estimate95 min
Human estimate65 min

2 · Set assumptions

Tune the agents

Cost uses the rates you set. It is illustrative, not a vendor quote. The model assumes one attempt per selected task and does not price retries.

3 · Compare the plan

Time, cost, and compounding risk

Human-only

—

— illustrative cost

One AI agent

—

— illustrative cost

Parallel agents

—

— illustrative cost

Clean-run probability

—

Chance every selected agent task succeeds on its first attempt, using your per-task reliability setting. This is why more workers do not automatically mean a dependable result.

Critical path

—

Extra agents only help tasks outside this dependency chain.

Parallel-agent timeline

Independent evidence work overlaps. Analysis and synthesis still wait.

Not to exact scale on narrow screens

Practical walkthrough

How to use the AI agent workflow simulator

1. Map the research tasks

Choose a market landscape, technical due-diligence, or evidence-review workflow. Keep only the tasks the project needs, then estimate the human effort for each one.

2. Set the agent assumptions

Change the number of parallel agents, expected speed, per-task reliability, hourly cost, project complexity, and final review time. These inputs are explicit so you can replace the defaults with your own evidence.

3. Read the bottlenecks

Compare elapsed time and cost, then inspect the critical path and clean-run probability. Add agents only where independent tasks can overlap; keep review gates where evidence or consequences demand judgment.

What the model does

It exposes the coordination tax

The simulator treats research as a dependency graph. It assigns ready tasks to the next available agent, then waits at merge points. Parallelism does not remove dependencies: framing precedes collection, analysis waits for evidence, and synthesis waits for analysis.

What it deliberately does not claim

Speed is not judgment

A faster model cannot approve its own assumptions. It cannot decide whether a missing source is acceptable, whether two measurements are comparable, or whether the conclusion is consequential enough to publish. Keep a human review gate where those decisions matter.

Why this exists now

The recent development behind the lab

OpenAI reported that its researchers increasingly use coding agents in concurrent sessions, while still keeping people responsible for priorities and decisions about which results to pursue. Its own write-up also warns that research has bottlenecks, so overall progress should not be inferred from isolated activity metrics.

In a separate customer example, Parallel reported completing one labor-market research task in half the time and at roughly half the code cost with GPT-6 Astra, and described dividing work among sub-agents. OpenAI's Astra launch reports stronger computer-use performance at lower elapsed time on one benchmark. These are vendor and customer-reported examples, not universal speed multipliers—which is why every assumption in this lab is adjustable.

Primary sources

Want to test the agents rather than estimate them? Use Learnetto's practical AI evals guide to turn this workflow into a task suite with observable pass criteria.

FAQ

AI research workflow questions

What is an AI agent workflow?

An AI agent workflow divides a job into tasks, dependencies, tool calls, and review gates. Independent tasks can run concurrently, while dependent tasks must wait for prerequisite work. This simulator models that structure for research projects.

When do parallel AI agents save time?

Parallel AI agents save time when several independent tasks are ready at once—for example, source discovery, data collection, and expert-perspective review after the question is framed. They do not shorten a dependency chain in which each task needs the output of the previous task.

Why include human review in an AI research workflow?

Human review is needed to assess evidence quality, resolve conflicting sources, challenge assumptions, and decide whether the result is safe or useful enough to act on. The simulator treats that review as visible work rather than hiding it inside an optimistic agent estimate.