The Journey to mainnet version 2
Fetch.ai is running three themed, incentivized testnets to take us to mainnet version 2. Each testnet will run for approximately 6 weeks, and will be arranged in groups of incentives. The below diagram outlines how this will work. When each incentivized testnet is complete, it will be reset and the new one created. This will reset state, and testing and work will focus on the new functionality added. With each step along the way, we get closer and closer to a fully featured Fetch V2 mainnet.
Throughout this process, Fetch.ai is running a stable testnet called Agentland. Agentland will not have its state reset, and is perfect for application and agent development and deployment.
When the funky incentivized testnet is complete, mainnet v2 will be launched.
Three phases of JourneyNet to Mainnet V2
The three phases are:
Phase 1: incentives are agent themed with some governance
Phase 2: incentives relate to the DRB, node operation and governance
Phase 3: incentives designed to deliver oracles and connect worlds with trustworthy information
Each phase lasts about six weeks and is sub-divided up to provide the broadest opportunities for all members of the community, as well as ensuring we focus testing efforts in the areas that ensure an on-time delivery of mainnet v2.
Phase 1's agent-focussed sub-sections are referred to as AW1, AW2, AW3 and AW4.
Phase 2's node and DRB sub-sections are referred to as BW1 to BW6.
Phase 3's oracle sub-sections are referred to as OW1 upwards.
More details on each of these will be announced as we get closer, and Fetch.ai may add additional sub-sections in order to test specific features, such as smart-contracts and other such technologies.
They're open throughout
Agent development is not limited to phase 1 of the testnet. We run a permament testnet called Agentland which is stable throughout this process. Agentland is where we are deploying and developing applications in hospitality, delivery, transportation, supply chains and more. Join us, and our increasing population of agents, to explore new applications and opportunities.