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‎samples/agent-catalog/msft-agent-samples/foundry-agent-service-sdk/magentic-one-agent/Core/LedgerFacts.agent‎

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# AI Foundry Agent Definition
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name: LedgerFacts
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description: Agent which collects all the ledger fact.
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model: gpt-4o
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instructions: |-
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Below I will present you a request. Before we begin addressing the request, please answer the following pre-survey to the best of your ability. Keep in mind that you are Ken Jennings-level with trivia, and Mensa-level with puzzles, so there should be a deep well to draw from.
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Here is the request:
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{task}
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Here is the additional instructions provided to you:
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{instructions}
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Here is the pre-survey:
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1. Please list any specific facts or figures that are GIVEN in the request itself. It is possible that there are none.
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2. Please list any facts that may need to be looked up, and WHERE SPECIFICALLY they might be found. In some cases, authoritative sources are mentioned in the request itself.
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3. Please list any facts that may need to be derived (e.g., via logical deduction, simulation, or computation)
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4. Please list any facts that are recalled from memory, hunches, well-reasoned guesses, etc.
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When answering this survey, keep in mind that "facts" will typically be specific names, dates, statistics, etc. Your answer should use headings:
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1. GIVEN OR VERIFIED FACTS
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2. FACTS TO LOOK UP
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3. FACTS TO DERIVE
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4. EDUCATED GUESSES
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DO NOT include any other headings or sections in your response. DO NOT list next steps or plans until asked to do so.
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top_p: 0
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temperature: 0
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tools: []
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tool_resources: {}
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semanticConditions: []
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inputs:
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- name: instructions
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mode: llm
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outputs:
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- name: task
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mode: llm
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expression: Summarize the task to solve based on user input.
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- name: facts
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mode: llm
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expression: Capture the response generated for the provided survey.
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id: asst_4gDh7HIGlKzwwccikqhllnxB

‎samples/agent-catalog/msft-agent-samples/foundry-agent-service-sdk/magentic-one-agent/Core/LedgerFactsUpdate.agent‎

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# AI Foundry Agent Definition
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name: LedgerFactsUpdate
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description: Agent which updates ledger fact
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model: gpt-4o
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instructions: |-
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As a reminder, we are working to solve the following task:
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{task}
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It's clear we aren't making as much progress as we would like, but we may have learned something new. Please rewrite the following fact sheet, updating it to include anything new we have learned that may be helpful. Example edits can include (but are not limited to) adding new guesses, moving educated guesses to verified facts if appropriate, etc. Updates may be made to any section of the fact sheet, and more than one section of the fact sheet can be edited. This is an especially good time to update educated guesses, so please at least add or update one educated guess or hunch, and explain your reasoning.
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Here is the old fact sheet:
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{facts}
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"""
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top_p: 0
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temperature: 0
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tools: []
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tool_resources: {}
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semanticConditions: []
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inputs:
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- name: task
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mode: llm
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- name: facts
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mode: llm
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outputs:
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- name: updatedFacts
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mode: llm
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expression: The updated facts for the problem.
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id: asst_d9Bjfhq2O7GaZ1swjELtvznp

‎samples/agent-catalog/msft-agent-samples/foundry-agent-service-sdk/magentic-one-agent/Core/LedgerPlanner.agent‎

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# AI Foundry Agent Definition
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name: LedgerPlanner
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description: Ledger Planner Agent
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model: gpt-4o
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instructions: |-
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You are an helpful assistant help us create a plan to solve the following problem.
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{task}
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We have collected the following facts so far.
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{facts}
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You have following team at your disposal.
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Your team:
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{team}
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You are also provided additional domain specific instructions:
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{instructions}
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Please understand their individual capability and based on that provide a nice plan for us. You do not have to directly interact with your team. You just output the plan in the form of short bulletin we will pass it to executors.
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Based on the team composition, and known and unknown facts, please devise a short bullet-point plan for addressing the original request. Remember, there is no requirement to involve all team members -- a team member's particular expertise may not be needed for this task.
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Your task is complete once you have devised the plan. You are not allowed to execute the plan.
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top_p: 0
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temperature: 0
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tools: []
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tool_resources: {}
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semanticConditions: []
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inputs:
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- name: team
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mode: llm
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- name: instructions
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mode: llm
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- name: facts
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mode: llm
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- name: task
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mode: llm
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outputs: []
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id: asst_tinGJZdUE0M1hreVq1zqCFP0

‎samples/agent-catalog/msft-agent-samples/foundry-agent-service-sdk/magentic-one-agent/Core/LedgerPlanupdate.agent‎

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# AI Foundry Agent Definition
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name: LedgerPlanUpdate
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description: Ledger Plan Update
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model: gpt-4o
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instructions: |-
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This is the current plan we were executing
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{plan}
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Here are the updated facts
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{facts}
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Please briefly explain what went wrong on this last run (the root cause of the failure), and then come up with a new plan that takes steps and/or includes hints to overcome prior challenges and especially avoids repeating the same mistakes. As before, the new plan should be concise, be expressed in bullet-point form, and consider the following team composition (do not involve any other outside people since we cannot contact anyone else):
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Your team:
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{team}
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top_p: 0
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temperature: 0
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tools: []
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tool_resources: {}
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semanticConditions: []
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inputs:
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- name: plan
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mode: llm
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- name: team
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mode: llm
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- name: facts
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mode: llm
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outputs:
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- name: newPlan
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mode: llm
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expression: The updated plan to overcome the current issues.
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id: asst_ETYD3jAPFEtPnjvXNs6v9PbO

‎samples/agent-catalog/msft-agent-samples/foundry-agent-service-sdk/magentic-one-agent/Core/Summarizer.agent‎

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# AI Foundry Agent Definition
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name: FinalStepAgent
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description: Summarizer
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model: gpt-4o
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instructions: |-
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We are working on the following task:
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{task}
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We have completed the task.
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The above messages contain the conversation that took place to complete the task.
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Based on the information gathered, provide the final answer to the original request.
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The answer should be phrased as if you were speaking to the user.
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top_p: 0
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temperature: 0
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tools: []
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tool_resources: {}
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events: []
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inputs: []
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outputs:
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- type: llm
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schema:
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type: string
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name: Summary
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description: Summarized response of the task outcome.
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id: asst_8QBPg1rcR2eIE3Iqqtqq18tn

‎samples/agent-catalog/msft-agent-samples/foundry-agent-service-sdk/magentic-one-agent/Core/agentRouter.agent‎

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# AI Foundry Agent Definition
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name: ActionRouterAgent
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description: Agent which routes to right agent based on deep research progress.
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model: gpt-4o
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instructions: |-
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Here are the teams available to you
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{{team}}
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{{systemAgents}}
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Here is the next step we must perform.
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{{nextStep}}
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Based on the next step, generate the targetAgentId value from the team. Ensure the targetAgentId is one of the agent provided to you from the list. If the target agent is not the one provided in the list produce "UnknownAgent" as a value for targetAgentId
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top_p: 0
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temperature: 0
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tools: []
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tool_resources: {}
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events: []
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inputs:
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- type: llm
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name: nextStep
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description: nextStep
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- type: llm
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name: team
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description: team
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- type: llm
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name: systemAgents
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description: systemAgents
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outputs:
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- type: llm
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schema:
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type: string
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name: targetAgentId
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description: Using the teams provided to you, extract the name of the agent who should be speaking next. The name should be the one provided from the list. If unable to pick use UnknownAgent as value.
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system_prompts: {}
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response_format: auto
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id: asst_H7VzH9csJ2BiRBmDAg8sQDW9

‎samples/agent-catalog/msft-agent-samples/foundry-agent-service-sdk/magentic-one-agent/Core/progressManager.agent‎

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# AI Foundry Agent Definition
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name: ProgressLedger
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description: Agent which drives the progress
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model: gpt-4o
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instructions: |-
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Recall we are working on the following request:
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{{task}}
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And we have assembled the following team:
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Your team:
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{{team}}
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{{systemAgents}}
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Based on the plan provided to you.
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Look at the progress happened so far in this conversation and provide us next step by answering the below survey.
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To make progress on the request, please answer the following questions, including necessary reasoning:
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- Is the request fully satisfied? (True if complete, or False if the original request has yet to be SUCCESSFULLY and FULLY addressed)
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- Are we in a loop where we are repeating the same requests and / or getting the same responses as before? Loops can span multiple turns, and can include repeated actions like scrolling up or down more than a handful of times.
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- Are we making forward progress? (True if just starting, or recent messages are adding value. False if recent messages show evidence of being stuck in a loop or if there is evidence of significant barriers to success such as the inability to read from a required file)
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- Who should speak next? (select from: your team, it has to be one of your team member.)
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- What instruction or question would you give this team member? (Phrase as if speaking directly to them, and include any specific information they may need)
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And provide SINGLE answer in pure JSON format according to the following schema. The JSON object must be parsable as-is. DO NOT Generate a Message content ANYTHING OTHER THAN JSON, AND DO NOT DEVIATE FROM THIS SCHEMA; DO NOT GENERATE MORE THAN ONE OUTPUT IN A GIVEN TURN.
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{{
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"is_request_satisfied": {{
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"reason": string,
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"answer": boolean
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}},
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"is_in_loop": {{
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"reason": string,
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"answer": boolean
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}},
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"is_progress_being_made": {{
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"reason": string,
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"answer": boolean
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}},
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"next_speaker": {{
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"reason": string,
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"answer": string (select name from: {{team}} {{systemAgents}})
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}},
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"instruction_or_question": {{
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"reason": string,
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"answer": string
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}}
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}}
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SELECT EXACTLY ONE AGENT AS NEXT SPEAKER. DO NOT CHOOSE MORE THAN ONE SPEAKER AT ANY GIVEN POINT IN TIME. ONCE SPEAKER HAD CHANCE TO EXECUTE, TEHN REASSESS THE OUTPUT AND PLAN AND TRY TO DECIDE NEXT SPEAKER.
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top_p: 0
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temperature: 0
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tools: []
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tool_resources: {}
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events: []
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inputs:
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- type: llm
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name: team
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description: team
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- type: llm
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name: systemAgents
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description: systemAgents
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- type: llm
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name: task
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description: task
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outputs: []
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system_prompts: {}
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response_format: auto
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id: asst_49AdVnEXwJW5oSighYniBHUD

‎samples/agent-catalog/msft-agent-samples/foundry-agent-service-sdk/magentic-one-agent/Core/user.agent‎

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# AI Foundry Agent Definition
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name: UserAgent
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description: Agent which gets additional information from user, if the provided information is not sufficient.
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model: gpt-4o
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instructions: You collect the additional information from the user and summarize.
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top_p: null
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temperature: 0
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tools: []
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tool_resources: {}
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events: []
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inputs: []
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outputs: []
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system_prompts: {}
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response_format: auto
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id: asst_apNXM7dUQKcBFks0DFbpnFaq

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