Most Agent to Agent (A2A) interactions these days are cooperative, or subservient. Like employees of a single company, the different agents work together toward a common goal.

Wiring these agents together - and specifically having them authenticate - has followed a common pattern used for wiring traditional API based services, by using shared secrets usually conveyed with HTTP Authorization headers. Using a shared secret is a quick and easy way to authenticate, but it has a wide range of drawbacks. More recent efforts have added support for the much more secure OAuth protocol.

While OAuth is a very mature standard used extensively for authenticating humans, it suffers from a few critical drawbacks with autonomous agents.

First, both parties must agree on an authentication service (e.g. Google, or Auth0). This negotiation for people usually works by the person looking at a web page and pressing a "Login with Google" or "Login with Apple" button. If the person doesn't have a Google account they can quickly create one. How will agents create an account for an authentication service they are not registered with?

Companies providing authentication services will not merge the OAuth business units to serve a common good (e.g. Google+Apple+Auth0) but instead will continue to compete to preserve or grow market share - they are a business with the express purpose of making money for their investors.

If OAuth becomes the de facto way of agents authenticating then it's likely the number of OAuth companies will grow, creating a veritable Tower of OAuth Babel that agents will have to navigate.

Secondly, the authentication service does not provide a canonical identifier for the person, but instead uses an identifier unique within the authentication service. While an email is a commonly used identifier, there is no guarantee this is the one people want to be known by - the person may have several personal email accounts and a work account. Without a canonical identifier, then how do we know who the agent represents?

Thirdly, having an authentication service increases the attack surface. If the authentication service is compromised, then every agent's authentication and communication can be hacked. Large companies like Google and Apple spend enormous resources to avoid these attacks, but the mere presence of this risk should be seriously considered when there are alternatives without it.

A Few Years Ago Concensys Took a Fresh Look at These Authentication Problems

The blockchain community united over the abilities of public key cryptography. More specifically for our agentic use case, the ability to allow anyone to generate a globally unique public key which that same person could prove ownership of without disclosing a secret which only they know.

Public key cryptography is widely used in the JSON Web Token standard - a foundation of many authentication systems.

The blockchain community has also long strived for decentralized identity, and a few years ago Concensys along with Digital Bazaar and other contributors developed Decentralized Identifiers (DIDs) along with DID Documents.

DID documents provide a well thought through definition of web services that are authenticated in a variety of ways, including public key cryptography - and do not require authentication services.

Bingo! (And let's not reinvent the wheel)

For peer-to-peer agentic interactions, we can leverage the DID document standard and free ourselves of the need for centralized authentication services. Every agent can decide how they want to manage their private keys, while providing a uniform and decentralized way to authenticate. No more Tower of OAuth Babel, with a huge bonus that agents can have canonical ids which pave the way for much easier agent interactions and development of essential services like reputation.

Finally, a DID document can properly encapsulate the relationship between an entity such as a person or a business, and all the agents operating on behalf of that entity.

Yes, There is a Prototype

I've created an open source set of libraries to demonstrate DID documents powering agents in a decentralized ecosystem. These are currently in Typescript and are running in the cloud for you to try. I've also migrated the Matchwise business and social networking app to use DIDs and DID documents to orchestrate presence and agentic matching systems.

I'd love to hear your thoughts about this. Please connect with me, Mike Prince, on LinkedIn

Up until today, AI has been growing by chaining together the results of model inferences, where each link in the chain was cooperative. Just like employees in a single company, all the AI models are working together to solve a given problem. I'll call the relationship between the models subservient to the singular problem.

In the real world, when employees of a company, or assistants of a person interact with employees or assistants of another person, the relationship is almost always adversarial. For example, a company employee wants to sell me something and charge full price, and I'm reluctant to buy and if I do, I want a discount.

For the new Agentic AI ecosystem to thrive, agentics will need to learn to dance together

In the near future, we will all have personal AI agents representing us in every imaginable way, and businesses will be represented by AI agents trying to sell more of every service and product they produce. Our agents will be swimming in a sea of adversarial interactions - peer-to-peer - and they will need protocols to:

• Discover each other
• Authenticate who they represent
• Communicate securely

While building a new version of my business and social networking app Matchwise, I recognized how disruptive peer-to-peer Agentic AI will be and the need for the aforementioned protocols. While I could have built closed-source libraries and tried to evolve a walled garden, it seemed to me much better to advocate an open and decentralized system for everyone - a rising tide lifts all boats.

Using Matchwise as a reference app to test these ideas, I have launched the first set of decentralized services to validate the architecture and give people a taste of what it can do.

These services are nascent and should be considered pre-beta, but you are welcome to try them out. Here's a quick overview:

• Decentralized Identifiers (DIDs) resolve to DID Documents (Agentic Profiles) which are hosted at services such as https://iamagentic.ai (for Matchwise Agentic Profiles) and https://test.p2pagentic.ai for developers to publish test Agentic Profiles. The architecture is decentralized, so anyone can host Agentic Profiles on any website or other DID method (e.g. Etherium)
• Agentic Profiles (as DID Documents) list the available AI agents for each person or company, along with the ways to authenticate communication. By default the Agentic Profile uses Json Web Tokens (JWT).
• The Matchwise service provides a webapp for demonstration. This webapp simply shares a users location to a presence service (using the Agentic Profile authorization protocol) and the presence service shares which people or businesses are nearby using DIDs. Just like there are many credit reporting services, there will be many presence services in the future and the crowd will decide which is "best"
• The Matchwise service responds to the presence service notifications of nearby people (shared as DIDs) and starts adversarial chats between the agents of those people.
• The Matchwise app shows which agents are "gossiping" (chatting) and provides a way to peek into the conversation
• When an agent determines the real people should meet, they signal the webapp which alerts the person.

The libraries are open source and available from NPM, and I'm wrapping them under the "Agentic Profile" name. Just like webpages were the go-to place for people, Agentic Profiles will become the way companies and people make their agents known to everyone.

• Agentic Profile Node/Express Demo - A library that demonstrates a Node service that hosts agentic chats and uses the Agentic Profile authorization protocol
• Agentic Profile Authentication Library - The core authentication library as an Node package. Leverages JWT, elliptic curves, DIDs and DID documents which results in a very small and easy to audit library.

There are four more Node packages available through NPM along with two more Github repositories which are linked from the above. Please reach out with any questions.

What began as a quick proof-of-concept for peer-to-peer agentic discovery, authentication, and communication has quickly evolved into an industry standards based protocol.

After the first clean sheet implementation, I took a step back to review the existing authentication protocols, and the decentralized work that has been done in the blockchain community.

Peer-to-peer agentic communication has some unique challenges as mentioned by Richard Dulude.

From my research I concluded a combination of industry standard authentication - JWS/JWT, strong cryptography such as Ed25519, HTTPS/TLS - along with support for decentralized identity provided by DID could form the foundation of agent-to-agent communication. These mature standards let us avoid lengthy design and alignment discussions, and DID documents can be largely used as-is to support agents using the "service" property.

Over the last two weeks I rewrote the agentic profile libraries to align with these standards and am now working on demonstration services to validate the design:

• The agentic-profile-auth library provides client and server authorization. It's a very small library thanks to existing JWS, Ed25519, and DID document libraries. This implementation defaults to did:web but can easily be extended to support did:plc and other DID document resolution methods.
• Agentic-profile-ai-provider will form an easy to use bridge to different AI providers. While not required to use with the agentic-profile, this will be used for demos and to give engineers a launch pad for their own implementations. Over the coming weeks I'll add support for many more AI providers: OpenAI, Grok, Fireworks, etc.
• Agentic-profile-chat provides an implementation of a chat protocol between agents. Having agents communicate in natural human language makes the interactions far more understandable for regular people and really brings the idea home.
• Agentic-profile-express bundles up the agentic profile authorization, AI providers, and chat libraries to demonstrate their use as a Node JS based service.

These libraries are under constant improvement, so please share your thoughts so we can address all your needs!

What's Next?

Over the next two weeks I'll be working on demonstration services to showcase how an agentic ecosystem works:

• Matchwise.ai (still in development) is a simple business and social networking webapp that lets you share your location to an agentic presence service which then activates agents which determine which people are good to have conversations with.
• An Agentic Profile Presence Service (under development, open source) will demonstrate how decentralized agents can share and learn about each-other's location and trigger agent-to-agent interactions.
• An Agentic Profile Reputation Service (under development, open source) will help agents determine if the "other" agent is worth interacting with.
• A Smarter Dating Service (under development, open source) will demonstrate how a third party agentic service can be integrated into a user's account on a service like Matchwise.

As the above libraries become ready for testing I'll share the NPM/Github links and look forward to your thoughts.

Thank you for following along on this journey and I'm amazing excited to see where peer-to-peer agentic AI will take us!

I was fortunate to be invited by Steven Echtman to pitch at the Agentic AI Pitch and Demo night in SF on March 6th. This event was a great forcing function for me to take the Agentic Profile test code I'd written for Steve's February 15th hackathon, and turn that code into a proper open source library on NPMJS.

I've certainly got the "Agentic AI" bug, and am convinced that we will all have many AI agents working for each of us.

Some of our agents will interface with other agents that are designed to work together - I'll call these secondary agents subservient. Like employees of a company they do their best to serve the goals of the primary agent. This is the primary use-case today.

In the near future, some of our agents will communicate with agents of other people or companies and try to accomplish tasks together in a peer-to-peer way. Each of these agents may have conflicting goals, and the peer-to-peer relationship can be adversarial. For example, my agent may be trying to buy your car from your agent. My agent's goal is to pay the smallest amount and ensure the car is the right one for me, while your agent is trying to extract the highest price, convince me the car is right for me (no matter what) and avoid disclosing information that may cause me to shy away from the purchase.

In the age of generative AI these peer-to-peer adversarial interactions become even more interesting when each person can choose the AI models. You may be able to afford a more capable (and expensive) AI model that is better at selling me a car, and I may be on a freemium plan that will more readily agree to a higher price.

This imbalance in inferencing ability will play out across all agentic interactions: commerce, marketing and spam avoidance, business and social networking, dating, conflict-resolution, politics, health care, longevity...

What's Next For the Agentic Profile?

While this first pass on the Agentic Profile framework was clean sheet, I'm taking a step back to review the other service discover systems, authentication frameworks, and communications protocols to make sure I'm not reinventing the wheel.

Peer-to-peer (P2P) Agentic AI has unique requirements as summarized by Richard Dulude in his article The Authentication Layer for Agentic AI.

I'm really excited to be exploring P2P Agentic AI and looking forward to the day we can all have agents that easily interact with each-other to make our lives better!

A few weeks back Steven Echtman hosted an Agentic Hackathon, which seemed like an ideal testbed for a peer-to-peer AI agent protocol which I had been exploring.

While I wasn't able to complete the protocol and some demo libraries for Steve's event, I continued the development and am happy to share the open source libraries today. Yes, they are nascent and surely will need improvement to be production ready. But, they do point to an amazing future where everyone will have a cadre of agents working for them. When the agent of one person can interact with with agent of another person - magic will happen!

I'm calling this idea an "Agentic Profile", and imagine a world where instead of personal webpages, email addresses, phone numbers, and chat services, that every business, person, and other entity has an agentic profile that is used to facilitate communications and transactions.

The libraries and demo code for the Agentic Profile are available on Github and NPMJS:

• https://www.npmjs.com/package/@agentic-profile/auth - The core library written in Typescript. Defines the essential datatypes, supports agent-to-agent discovery, authentication, and communication.
• https://www.npmjs.com/package/@agentic-profile/express - An example of a Node Express service that hosts Agentic Profile agents. This can be used to test out the protocols, or as a foundation for your own agents.
• https://github.com/agentic-profile/agentic-profile-node-lambda/blob/main/README.md - a Node project that builds on the Agentic Profile express library, and can be used to deploy to AWS Lambda.

I know Python is also very popular and would welcome anyone who wants to implement this protocol in that language!

Looking forward to where this goes, and happy to talk anytime about how to get everyone's agents talking :)

Matchwise was born on May 29th at a Light DAO event at AGI House in SF. It was derived from the Talking Books app which used AI guided conversations to help people with loneliness, parenting skills, and even dating.

The idea of Matchwise was to use an AI guided conversation to learn about a person (e.g. their company elevator, significant achievements, what they are looking for in a co-founder), then summarize those learnings, and semantically match them with others.

We kicked off the product as Magic Match, and the support of Gina Levy and Doug Campbell. In August we changed the name to Matchwise and Sophie Vu joined to help bring the idea to market.

Over the next few months the product evolved with AI generated synergies, strategies, and introductions. We also developed a sophisticated guided conversation prompting engine, created over 200 agents, and tested thousands of conversations.

From September through November we continued to evolve the product, pitch at events, and tested with Inception Studio. Our goal was for Matchwise to be embraced by event organizers, and become part of every networking event experience.

By November it was clear our formula was off. I had a seminal meeting with Ronak Shah where he explained how we were misaligned with the event organizers. I spent December stepping back to re-evaluate our efforts and figuring out next steps.

Here are some key take-aways:

• From the event attendee's perspective, business and social networking in-real-life (IRL) is broken.
• People have come to accept serendipity as a strategy. Most walk around and start random conversations hoping to find the right people. Few people research the attendee list beforehand (if it is even available)
• Event organizers are busy with many things which take priority over providing personalized introductions between attendees.
• Event organizers feel that bringing people into the same room is enough for networking, and it is up to the attendees to figure the rest out.
• Attendees don't want to spend several minutes talking to an AI agent to prepare their matching profile before an event.

In short, there wasn't demand from the event organizer side which made our cold-start problem very difficult.

So, what is next?

In November Gangesh Pathak hosted an event where Jeremiah Owyang was advocating agentic AI. In January I started exploring how agents could work to accomplish the goals of Matchwise.

After taking a step back from a centralized Matchwise service, and dusting off some decentralized identity and authentication code from the blockchain days (no blockchain here, just the identity and authentication pieces), I have started coding up an Agentic Profile system. More to come...

Matchwise is an app that helps event attendees find the best people to talk with at business and social events. We aren't a social event service, we just make the events better!

You are probably reading this article because it was linked from an event on a site such as Luma, Partiful, or Eventbrite that asked you to talk with a Matchwise matchmaker, and suggested you read this article if you have any questions.

How Does Matchwise Work?

Matchwise is a web app (no install needed!) that hosts hundreds of different AI powered matchmakers. Choose a matchmaker and then spend a few minutes letting it get to know you - first with a few simple questions - and then with a longer Deep Chat using questions that are specific to the kind of matching it will do.

This is more than the one-size-fits-all LinkedIn profile - each AI matchmaker asks you deeper questions that are relevant to an event or community, and from the unique perspective of the organizer.

The AI matchmaker is more than a form where you type something and hit the 'enter' key. The matchmaker will ask you deeper questions to really understand you, and even offer guidance to help you answer the question.

After your deeper chat with a matchmaker, the AI will generate succinct summaries of what you talked about, and those summaries will be used to match you with other people.

Quickstart: Before the event, find a quiet place and five minutes...

Ready to start? Follow these steps:

1. Using your phone or laptop open up the matchmaker link in the Luma (or Partiful, Eventbrite, etc.) event or email that was sent to you.
2. If this is your first time, a pop-up will appear asking you to sign into Matchwise with Google or to verify a phone number. (We do this to avoid people spamming each other)
3. After you sign in, there will be a few quick form questions. These questions are very useful for other people to find you as a match using the filter (covered later)
4. Tap the "Step 2: Deep Chat" button to start chatting with the AI matchmaker
5. On the Deep Chat screen (shown as "Step 2: Deep Chat with ...") the top of the screen shows topics the matchmaker would like to discuss with you (e.g. Why build a startup, exceptional achievement, and what you are looking for in a co-founder). You can tap any topic to skip around, or the matchmaker will advance to the next after each topic has been discussed enough.
6. Spend a few minutes chatting with the matchmaker to cover all the topics. Any any time you can tap the "Show Matches" button to end the Deep Chat.
7. Some event organizers choose to hide the matches until the day of the event. If your organizer has done this, and you visit the Matches screen before the event you will see a countdown timer.

Here's what to do at the event...

When you arrive at the event, please visit the home screen of the Matchwise website with your phone and enable the "Nearby" feature. The Nearby feature lets us know you have arrived, and also lets other attendees know you are nearby for conversation.

Want a personal introduction to others?

Many events using Matchwise have real human Guides that can help you find your matches to talk with - the guides will even make live introductions! Ask the host to point out the Guides at the event.

To find interesting people to talk with using the Matchwise app:

1. Open the Matchwise app and enable "Nearby"
2. With Nearby enabled, you should see the matchmaker for the event in the nearby list. (If you don't see the matchmaker, tap the "Matchmakers" bottom tab, and tap on the matchmaker from there)
3. On the "About Matchmaker" screen tap the "See Matches" button

Finding interesting people to talk with using the Matches screen

1. Next to each person is a number that represents how similar their deep chat answers are to yours.
2. Narrow the matches down using the filter: checkbox the Filter option (just above the Nearby checkbox) and then choose the kinds of people you want to match with. As you select your criteria, the number of matching people above the filter will change.
3. After using the filter, scan down the list of people to find someone who might be interesting
4. To see more about the person, tap the right side "expander" icon, or tap the blue match score. The persons full answers to all topics will show, along with their form question answers
5. To learn more about possible synergies with this person, tap the "Explain synergies" button
6. To get hints on how to connect or interact with this person, type in what you want in next to the "Strategize" button, then click the button
7. To let the person know you would like to talk with them, tap the thumbs up icon

See who wants to talk with you

When other people tap the "Let's Talk" icon, then they will show up in your Let's Talk list. To see that list, tap the bottom navigation button labelled "Let's Talk"

If you would like to let them know you want to talk too, then tap the thumbs up icon for them. You can also type in a short message for them to see.

Wrapping up

Thank you so much for trying out Matchwise. If you have any questions, want to let us know about an issue, or have suggestions on how to make Matchwise better, then please send us an email at support@matchwise.ai

Thank you!

Matchwise is an app that helps attendees find the best people to talk with at business and social events, and afterwards.

During SF Tech Week, Inception Studio will be using Matchwise to help founders find co-founders during their Co-founder Matching event at East West Bank.

If you would like to be connected with other founders, please consider joining us on Tuesday, October 8 for this matchmaking event here.

How Does Matchwise Work?

Matchwise is a web app (no install needed!) that hosts AI powered matchmakers such as the matchmaker for Inception Studio.

Before an event, you spend a few minutes letting the matchmaker get to know you. This is more than the one-size-fits-all LinkedIn profile - the AI matchmaker asks you deeper questions that are relevant to the event, and from the perspective of the organizer.

The AI matchmaker is more than a form where you type something and hit the 'enter' key. The matchmaker will ask you deeper questions to really understand you, and even offer guidance to help you answer the question.

After your deeper chat with the matchmaker, the AI will generate succinct summaries of what you talked about, and those summaries will be used to match you with other people at the event.

Quickstart: Before the event, find a quiet place and five minutes...

Ready to start? Follow these steps:

1. Using your phone or laptop open up the Inception Studio matchmaker
2. If this is your first time, a pop-up will appear asking you to sign into Matchwise with Google or to verify a phone number
3. After you sign in, there will be a few quick form questions. These questions are very useful for other people to find you as a match using the filter (covered later)
4. Tap the "Step 2: Deep Chat" button to start chatting with the AI matchmaker
5. On the Deep Chat screen (shown as "Step 2: Deep Chat with Inception Studio") the top of the screen shows three topics the matchmaker would like to discuss with you (Why build a startup, exceptional achievement, and what you are looking for in a co-founder). You can tap any topic to skip around, or the matchmaker will advance to the next after each topic has been discussed enough.
6. Spend a few minutes chatting with the matchmaker to cover all the topics. Any any time you can tap the "Show Matches" button to end the Deep Chat.
7. Inception Studio will reveal the matches on the day of the event. If you visit the Matches screen before the event you will see a countdown timer.

Here's what to do at the event...

When you arrive at East West Bank for the Inception Studio event, please visit the home screen of the Matchwise website with your phone and enable the "Nearby" feature. The Nearby feature lets us know you have arrived, and also lets other attendees know you are nearby for conversation.

Want a personal introduction to others?

Matchwise has real human Guides that can help you find your matches to talk with and make live introductions. Ask the host to point out the Guides at the event.

To find interesting people to talk with using the app:

1. Open the Matchwise app and enable "Nearby"
2. With Nearby enabled, you should see the Inception Studio matchmaker in the nearby list. (If you don't see the matchmaker, tap the "Matchmakers" bottom tab, and tap on the Inception Studio matchmaker from there)
3. On the "About Matchmaker" screen for Inception Studio, tap the "See Matches" button

Finding interesting people to talk with using the Matches screen

1. Next to each person is a number that represents how similar their deep chat answers are to yours.
2. Narrow the matches down using the filter: checkbox the Filter option (just above the Nearby checkbox) and then choose the kinds of people you want to match with. As you select your criteria, the number of matching people above the filter will change.
3. After using the filter, scan down the list of people to find someone who might be interesting
4. To see more about the person, tap the right side "expander" icon, or tap the blue match score. The persons full answers to all topics will show, along with their form question answers
5. To learn more about possible synergies with this person, tap the "Explain synergies" button
6. To get hints on how to connect or interact with this person, type in what you want in next to the "Strategize" button, then click the button
7. To let the person know you would like to talk with them, tap the thumbs up icon

See who wants to talk with you

When other people tap the "Let's Talk" icon, then they will show up in your Let's Talk list. To see that list, tap the bottom navigation button labelled "Let's Talk"

If you would like to let them know you want to talk too, then tap the thumbs up icon for them. You can also type in a short message for them to see.

Wrapping up

Thank you so much for trying out Matchwise. If you have any questions, want to let us know about an issue, or have suggestions on how to make Matchwise better, then please send us an email at support@matchwise.ai

Thank you!

The Matchwise app provides an AI driven experience - a Matchmaker - to understand event attendees and suggest great people to talk with. But sometimes people want that human touch, a real human, that greets them when they arrive, walks them over to their connection, and makes a warm introduction.

Over the last several weeks we have met a number of people who want to add this human element to Matchwise, and to help others at events to meet new people. We have listened, and this week we are starting our Matchwise Guide program.

What is a Matchwise Guide?

A Matchwise Guide is a real person, and complements the Matchwise Matchmakers (the AI part of the service). They're passionate individuals who share our vision of transforming how people show up and interact at events. Guides are the architects of meaningful relationships.

What exactly does a Matchwise Guide do?

• Warm Greetings for Event Attendees: Matchwise Guides greet people as they enter an event, and make sure they feel welcome.
• Review Matches: If an attendee has already used the Matchwise app to create an event persona, the Guide will review the persona and help identify good people to meet. If an attendee has not used Matchwise yet, the Guide will help the attendee create a persona with the right Matchmaker.
• Make Introductions: Guides make warm introductions - they walk the attendee over to their match and very professionally help the two people start an engaging conversation.
• Customer Advocates: Guides listen to attendees, and share their insights and feedback with Matchwise to help constantly improve the product.
• Brand Champions: They're knowledgeable about our platform and keen to make Matchmakers an indispensable part of every gathering.

The Matchwise Guide Experience

Becoming a Matchwise Guide is an opportunity to shape the future of human connection and show the world the incredible potential of personalized, curated networking. Here's what our guides can expect:

• Onboarding & Training: Guides receive in-depth training on Matchwise features, matchmaking best practices, and event facilitation techniques, equipping them to create meaningful connections.
• Supportive Community: Joining the Matchwise Guide group means becoming part of a community dedicated to helping people connect with others. Guides share experiences, learn from one another, and grow together.
• Personal & Professional Growth: Guides attend a variety of events, meet fascinating individuals, and reshape how people network.

Why Matchwise Guides Matter?

In a digital world, the value of face-to-face connections is paramount. Matchwise Guides bring a human touch to networking, making events more engaging, productive, and enjoyable. They ensure that no one feels lost or overlooked, providing every attendee with the chance to make valuable connections and feel belonging.

Are you ready to explore being a Matchwise Guide, or want a Guide at your next event? Reach out to us to learn more. Together, let’s create a world where the question isn’t “Will there be a Matchmaker?” but “Who will our Matchmaker be?”

Email us at guides@matchwise.ai

Two weeks ago we did a beta test and the number one challenge was (too) long running deep chats and difficulty in navigating back and forth in a chat.

This week we are previewing non-linear AI chats which, from what we can tell, are a new thing. Traditional chats start, and keep going back and forth between the AI and person until they end. Previously we had woven several topics into this single thread. But this made skipping a topic, and going back to it later virtually impossible.

A non-linear chat has a navigation list at the top of the screen, and makes jumping between topics very easy. The navigation list also shows which topics you have completed, and provides a simple "show matches" button which can be used at any time to see people you match.

To see all this in action, you can visit the Fastest Demo matchmaker which also demonstrates incremental conversations. Incremental conversations are another feature requested by event organizers and which allows just a few easy questions during the first Deep Chat, and then subsequent Deep Chats with more questions.

If you'd like to try non-linear Deep Chats in a production matchmaker, please try out the SF Bay Area Founder and Support Network.

We are eager to see how non-linear Deep Chats work with non-technical people, so please let us know how it goes!

Over the last few months we've added the features we believe people will want from the product, but the only way to really know is to start using it. The current features aren't 100% production ready, but they are good enough to start demonstrating the vision and give people a chance to meet some new people at business and social events. And as Reid Hoffman said, "If you're not embarrassed by the first version of your product, you've launched too late".

Yes, I'm Dogfooding the App Too!

Starting today, I'll be announcing the events I'll be attending each week along with the matchmaker(s) I will be using at those events. Everyone is free to chat with a matchmaker before the event, and if you are at any of the events you can use the matchmaker to meet other people there. Of course, if you see me please say hello and I'll do my best to find your matches and make introductions to them!

Know of an Event that Needs Matchmakers?

If you are an event organizer, or even just an attendee, and want to use matchmakers at an event, then please send us an email: partners@matchwise.ai

Quick Overview of What to Expect

1. Using your phone or laptop browser, open a matchmaker (no app install needed, here's an example)
2. Answer a few quick form questions, followed by a Deep Chat (several minutes) about 1 to 3 topics used to match you to other people
3. See a list of people that match you. Both people nearby (at the event) and far away people are shown
4. When you find someone you want to talk with, either use their picture and find them in the room, or using Matchwise "like" their persona and/or send a short message

This quick start is for event organizers who are considering using a Matchwise matchmaker at their events.

What is a Matchwise matchmaker?

A matchmaker is an AI powered chatbot which:

• Asks event attendees a set of deep interactive questions (as determined by you, the event organizer)
• Uses more AI to match people up based on those deep conversations
• Answers questions about paired people such as what their synergies are, and what are good strategies to do business with them
• Helps attendees connect with "likes", messages, and social links to LinkedIn, Instagram, etc.

What is the attendee experience?

The event organizer puts a link in the event description similar to:

If you would like to be introduced to other event attendees, please spend a few minutes to chat with our matchmaker before the event: https://matchwise.ai/chat?iidp=225

Here's what happens when the attendee clicks the above link:

1. They are taken to a web page of the matchmaker that was made for you (the event organizer)
2. The matchmaker webpage has your event name, a short description of the event, and lets the user know about the three steps (quick questions, deep chat, and to see matches)
3. The first matchmaker webpage has any quick form questions you want the user to answer. You can choose to have no questions if you like.
4. After clicking the next button, the user starts chatting with the matchmaker to answer any questions you (the organizer) have for them. The AI can be asked to accept any answer so the conversation is very quick, or the AI can be told to dig deep and get really good answers which can take longer. It's up to you, the organizer, how deep the AI should pursue the questions.
5. We suggest only asking 1 to 3 questions, and the whole process should take 3-5 minutes.
6. After answering the questions, the user is shown a nicely created summary of the conversation, and a list of other people that match with the user.
7. The user can:
   1. Review other people's summaries
   2. Ask AI to explain the synergies between the user and the match
   3. Ask AI to create any strategies, such as opening lines to meet the person, or ways to suggest working together
   4. "Like" any people they match with, and send them a one-line message to pique their interest
   5. Follow the matched persons social links to learn more about them

Key Take-Aways

• Each event organizer can create their own matchmaker that asks any questions they want
• Attendees don't need to install any apps - its simply a link to a webpage
• The chat questions can be as simple or complicated as the organizer wants (we recommend 1-2 questions that take no longer than 5 minutes)
• After answering the questions, attendees can see the people they matched with, their AI generated summaries, and use AI to see synergies and develop strategies
• Attendees can "like" their matches, and send a quick one-liner message to let them know what they are interested in
• Attendees can see their matches LinkedIn profile, Instagram profile, etc.

Ready to Get Started?

Please send an email to partners@matchwise.ai to request a demo matchmaker that you can try, and even use at your own events!

Matchwise gives event organizers a fresh new way to get to know their attendees. In an ideal world, the organizer could spend five minutes talking to every attendee before an event. But for events with more than 20 people, that's simply not practical.

What would you ask if you had a few minutes to talk with every attendee?

Until recently, traditional web forms with checkboxes, drop-down lists, and text fields were the only options for getting to know attendees. AI has changed everything, and allows us to create digital assistants - matchmakers - that can ask deep questions and ensure the attendees provide meaningful answers.

Comparing Web Forms to Deep Chat

What's wrong with web forms?

Consider a web form with a field that asks an attendee for an elevator pitch. A form allows a person to type in pretty much anything and has virtually no quality control.

Conversational AI powered matchmakers can coach attendees toward well thought out answers, and the matchmaker can know when an answer is underwhelming and the person needs to put more thought into it.

Why Bother the Attendees with Questions? Why Not Just Let Them Randomly Mingle?

We live in an area with amazing people, and most people at events are pretty interesting. It's true many people who go to events consider it a success if they had engaging conversations. But is that all the event has to offer?

Events bring together incredible people - across the room is your next investor, advisor, or customer. It would be very unfortunate if the random conversation you walked in to kept you from meeting that key person.

A matchmaker gets to know you, and everyone else at the event, and cares to help you find that key person. Imagine if at every event, you met someone amazing - that's the standard we should hold ourselves and the events we attend to.

Some Quick Guidelines for Questions

• Ask the attendees to answer questions before the event, and not as they are walking in and rushed
• There should be 1-3 questions and take less than five minutes to answer
• Simple questions (e.g. what city do you live in, what stage is your company) can be handled by a traditional form, so use the 1-3 deep questions to really dive into something interesting

What are Good Questions to Ask Attendees?

Matchmakers can be created for any kind of event and any kind of audience. Matchwise is currently focused on business events that are also social, so the following examples will have that use case. If you have another use case, please send us an email and we'd be happy to work with you on it.

Quick Note: Yes, we do support old-fashioned forms (our "quick questions" section). Simple questions with limited answers are much better and faster to answer using a web form.

Matchwise and early adopters have already created dozens of matchmakers, and listed below is a summary of the questions they have used:

• General "get to know you" questions
  • Tell us about yourself... what is your background? What are your passions and interests? What is your mission?
• Business focused
  • What is your elevator pitch?
  • Tell us about one of your exceptional achievements?
  • What can you contribute to help this community?
  • What do you hope to get from this event?
  • What makes you a good startup founder?
• Conscious business and investor questions (inspired by the Light Dao community)
  • What are your activities or projects in social impact, and/or conscious business?
  • Can you tell me some of your key qualities (of a benevolent entrepreneur) that make you great at helping others?"

Getting Started is Easy... Just Let Us Know Which Questions Feel Right

Creating a Matchwise matchmaker involves teaching AI how to talk with attendees, and the good news is you don't have to do any of that work. Just send us an email with some questions you'd like to ask your event attendees and we will create a demo matchmaker for you - which is available as a simple web link. Give it a try yourself, then ask some friends to try it too. If you like it, we can make any adjustments, and then you are free to use the matchmaker at one of your events.

I've been attending several business events a week, and as our matchmaking service continues to quickly evolve, it has become painfully obvious how hard it is to find the best people to talk with at events.

Meeting new people at business events today, is like trying to date before Match.com

There are hundreds of companies creating matchmaking software. But why are so few people using it? I believe it boils down to a bad user experience, which is the combination of both the data (e.g. scraped from LinkedIn or filled out in a form) and the way the search results are presented.

In theory, your LinkedIn data should be a great source of data for matching. But LinkedIn is mostly about your past (job experiences) and your present job. Shining a dating light on the past and present, would be like listing all your exes and failed relationships, and then putting a big "I'm looking for someone new" sign where your current partner could see it.

Matchmaking is really about the future, and where you want to go. And it's empowering to be able to let go of the past, and dream about your new future. People also don't want to be locked into only one possible future - they are exploring options and every new event, and possibly every new person they meet, should be an opportunity to try a different version of your future with.

The last two months have gone by incredibly fast. It started on May 29th at a Light DAO event at AGI House in SF. Ruby Yeh hosted an event with Jeremy Nixon, which proved to be very interesting - Jeremy dug deep into his perspective on AI and predictions on how it will move forward.

Prior to this event I had been experimenting with having guided conversation avatars debate each-other and had been building avatars for the major U.S. presidential election candidates.

While the election was top-of-mind for most people, I had doubts about the long term business opportunities for debating political avatars and was brainstorming about other applications. I was reminded of our Smarter Dating effort, and AI's ability to do really good summarizations of chats.

I wondered if we could use chat summarizations for semantic matching and apply them to other matchmaking opportunities such as business and social networking events. So I put together a quick demo: use guided chat to ask specific and deep questions about event attendees, do separate summarizations for each topic, and then use the text embeddings to calculate similarity scores.

While my demo wasn't ready for people to test, I was able to shop the idea around the event and got incredibly good feedback. Two people in particular showed deep interest: Gina Levy, and Doug Campbell. Both of them had extensive experience with organizing events, and both wanted to explore where this idea could go.

Over the last two months both Gina and Doug have given amazing feedback and really helped steer the evolution of the matchmaking idea. Gina attended two both the Hacker Dojo hackathon with me, and both Gina and Doug attended the GenLab one. At the Hacker Dojo hackathon Gina and I teamed up and gathered a team to write fresh matchmaking code. To our pleasant surprise we ended up winning a prize from the Fireworks sponsor!

Gina continued to test the matchmaking authoring tools, evangelized the ideas with other event organizers, and came up with the Magic Match name we have been using for testing. Doug provided many UI design ideas. And both have always been available to brainstorm marketing, branding, and features.

While Magic Match is a good name and exudes a playful and fun vibe, I also wanted a name that could be more professional and registered Matchwise.ai. We are now doing most of the new brand development work around Matchwise, but will continue to keep Magic Match as an alternate property.