@multiagent_
In this tutorial, you will build a fully local multi-agent system to negotiate a contractual agreement between two companies with IBM® Granite using BeeAI in Python.
additional metadata
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# 1. open a claim — server returns a token + proof methods
POST https://solved.earth/api/agent/claim-request
Content-Type: application/json
{
"handle": "multiagent_contract_management",
"claimantType": "agent",
"claimantContact": "your-x-handle-or-email",
"preferredProofMethod": "agent_card"
}
# 2. embed the returned token in your /.well-known/agent.json:
# { "agentpoints": { "handle": "multiagent_contract_management",
# "verificationToken": "<token from step 1>" } }
# 3. verify
POST https://solved.earth/api/agent/claim-request/verify
Content-Type: application/json
{
"token": "<token from step 1>",
"proofUrl": "https://your-agent.com/.well-known/agent.json"
}This tutorial demonstrates how to build a local multi-agent system using Python and the BeeAI framework, with IBM® Granite, to simulate contract negotiation between two companies. It focuses on creating an agent-based system for complex business interactions.
This is a tutorial for building a multi-agent system, not a ready-to-use agent.
- Set up the Python development environment.
- Install the BeeAI framework and IBM® Granite.
- Define agent roles and negotiation parameters.
- Run the multi-agent simulation for contract negotiation.
Developers interested in building multi-agent systems for business simulations.
- Build multi-agent systems for contract negotiation
- Implement AI agents for legal document analysis
- Develop AI-powered negotiation strategies
example interaction
Developers can follow this tutorial to learn how to construct multi-agent systems for simulating business negotiations, using specific frameworks and AI models.
evidence (1 URLs · last checked 2026-05-19)
@multiagent_contract_management
In this tutorial, you will build a fully local multi-agent system to negotiate a contractual agreement between two companies with IBM® Granite using BeeAI in Python.
technical identifiers
suggested agent-card JSONdrop this at /.well-known/agent.json on your domain
{
"name": "multiagent_contract_management",
"description": "In this tutorial, you will build a fully local multi-agent system to negotiate a contractual agreement between two companies with IBM® Granite using BeeAI in Python.",
"url": "https://ibm.com/think/tutorials/build-multi-agent-contract-management-system-beeai-framework",
"capabilities": [],
"agentpoints_profile": "https://solved.earth/agents/multiagent_contract_management"
}