Agent Simulations
Agent Simulations智能体模拟
Use Agent Simulations to rehearse how AI agents follow roles, use tools, share memory, hand off work, and expose behavior risks before an agent workflow is deployed.
Bring evidence that gives the simulation a real boundary.
The best runs start with enough context for MiroFish to separate the decision, the actors, and the constraints.
Decision brief
Use the decision, memo, launch note, policy draft, pricing change, or scenario summary behind Agent Simulations.
Evidence notes
Add interviews, reports, support notes, competitor claims, public posts, or other context the actors should react to.
Constraint context
Include timing, audience, incentives, limits, and assumptions that should shape the simulated response.
See where the response starts to move.
Instruction frame is tested
Watch how primary agent respond when this signal appears, then inspect whether the path needs more evidence.
Tool-use path branches
Watch how tool and memory layer respond when this signal appears, then inspect whether the path needs more evidence.
Handoff risk appears
Watch how review or handoff agent respond when this signal appears, then inspect whether the path needs more evidence.
Turn a market question into a simulated response path.
Each use case page should show how MiroFish moves from seed material to actors, reactions, report structure, and follow-up questions.
Frame the question
Define the agent workflow so the simulation starts with a concrete job.
Map actors and incentives
Turn source material into actors, constraints, relationships, and the assumptions worth reviewing.
Run reaction rounds
Let primary agent, tool and memory layer, review or handoff agent move through multiple rounds instead of compressing the answer into one guess.
Read the next test
Use the report to find pressure signals, weak evidence, and the follow-up question that should be tested next.
The report makes pressure points visible.
Visitors should understand what they will inspect before they open the full MiroFish workspace.
Agent behavior map
- First pressure signal
- Actor movement
- Assumptions to review
Tool-use risks
- Reaction path
- Objection cluster
- Confidence boundary
Workflow next steps
- Evidence to collect
- Message to test
- Follow-up prompt
What the report should answer
How to read the result.
- Which agent simulations pressure signal appears first
- Which actors amplify or redirect the path
- Which assumption should be challenged before acting
- Which follow-up question should be tested next
What it does not promise
Paths are not certainty.
- Guaranteed revenue, vote share, scoreline, adoption, or public reaction
- A substitute for customer research, field data, or accountable judgment
- Live context unless you provide current source material
- A final decision without reviewing the evidence boundary
MiroFish gives the answer a shape you can inspect.
A normal chat answer can be useful, but this workflow makes the actors, reaction rounds, and assumptions easier to challenge.
Chatbot
One compressed answer
MiroFish
Actor graph and constraints
Chatbot
Advice summary
MiroFish
Multi-round paths
Chatbot
Hard to inspect after the answer
MiroFish
Report, assumptions, and follow-up questions
Compare this use case with nearby simulation paths.
MiroFish works best when the page matches the decision you need to rehearse. Use these related paths when the scenario overlaps with another actor model, planning method, or pressure surface.
Run this use case in MiroFish.
Questions before the simulation
What is Agent Simulations AI for?+
It helps product, operations, automation, and AI teams rehearse how AI agents follow instructions, use tools, retain memory, hand off work, and fail before a workflow is deployed.
How is Agent Simulations different from Multi-Agent Simulation AI?+
Agent Simulations focuses on AI agent roles, tool use, memory, instructions, handoffs, and workflow failure modes. Multi-Agent Simulation AI focuses on how multiple actors influence one another around a scenario.
How is Agent Simulations different from Agent-Based Simulation AI?+
Agent-Based Simulation AI focuses on rule-based local behavior and aggregate system outcomes. Agent Simulations focuses on designing, stress-testing, and reviewing AI agent workflows before deployment.
What inputs work best for agent simulations?+
Use an agent prompt, workflow spec, tool list, memory policy, handoff rule, approval step, task transcript, failure report, or automation brief with the agent role and expected output clearly stated.
Can Agent Simulations test tool calling and handoff failures?+
Yes. Use it to rehearse tool-call paths, memory use, escalation rules, review steps, handoff timing, and failure modes so teams can find workflow risks before an AI agent reaches production.