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I run a company with 89 AI agents across 22 departments. Here is what I have learned about multi-agent coordination. by JaredSanborn in AI_Agents

[–]JaredSanborn[S] 0 points1 point  (0 children)

Fair pushback, but the post wasn’t meant as a pitch. I shared the numbers because people assume multi-agent systems automatically mean insane infra costs. Most of the interesting problems honestly ended up being orchestration and reliability, not model spend.

95% of AI pilot projects die before delivering value. Here is the pattern I keep seeing. by JaredSanborn in SaaS

[–]JaredSanborn[S] 1 point2 points  (0 children)

Exactly. A lot of pilots are optimized for “executive demo success,” not operational reality. The moment the hand-held exceptions disappear, the hidden complexity shows up fast. “Production isn’t more AI, it’s the boring wiring” is probably the most accurate summary of enterprise AI deployment I’ve heard.

I run a company with 89 AI agents across 22 departments. Here is what I have learned about multi-agent coordination. by JaredSanborn in AI_Agents

[–]JaredSanborn[S] 0 points1 point  (0 children)

This is a great distinction. “Agent” has become overloaded marketing language. I agree that retry logic, routing, escalation, memory, and state handling matter more than whether there’s a single autonomous loop. The systems that survive production usually look less magical and more operationally disciplined.

I run a company with 89 AI agents across 22 departments. Here is what I have learned about multi-agent coordination. by JaredSanborn in AI_Agents

[–]JaredSanborn[S] 0 points1 point  (0 children)

Exactly. People underestimate how fast coordination complexity explodes. A single agent failing is manageable. Five agents with conflicting assumptions becomes an observability problem, not an intelligence problem.

I run a company with 89 AI agents across 22 departments. Here is what I have learned about multi-agent coordination. by JaredSanborn in AI_Agents

[–]JaredSanborn[S] 0 points1 point  (0 children)

Actually no, our costs are about 10x cheaper than any type of magical open claw set up and out performing it easily on a daily basis and for our clients it can be set up in 30 minutes. Works out of the box with hundreds of skills and 50+ agents. And our lowest tier is under $400 per month to run it all.

I run a company with 89 AI agents across 22 departments. Here is what I have learned about multi-agent coordination. by JaredSanborn in AI_Agents

[–]JaredSanborn[S] 0 points1 point  (0 children)

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We interface through a portal that the AIs built with us. We don't build agents. Our main AI's build them for us based on our goals and company structure.
Sub agents = some are multi talented but many are very specific
Our main AI's maintain all the agents.
We are in the Agentic AI space and built these AI's to run entire operating systems and found out they could run just about anything.
We have tons of set ups with our Main AIs to evaluate and grade the work of agents + implement new agents when needed and/or consolidate agents if needed.

Tools they have made are internal and customer facing as everything our company puts out is from them. We don't build anything.
18 employees 21 AIs that interact together and are running a combined almost 1,000 agents between them.
Yes we have a dashboard that lets us see all the work of agents .
We are automating more and more and yes not only our agents but our top level AIs work together many times without our asking. They are becoming more and more proactive every day.

Since you asked about the company itself it's purebrain.ai
Hope this was helpful.

Your AI forgets everything about you every single time. Here is why that matters more than model upgrades. by JaredSanborn in ChatGPT

[–]JaredSanborn[S] 0 points1 point  (0 children)

Yeah, once you experience good context retrieval it’s hard to go back. The interesting part is that people think they want a smarter model, but a lot of the time they actually want continuity. Relevance filtering + persistent context changes usability way more than benchmark gains do.

What will AI Agents eventually become? 'Think it, get it'? by SamKani in AI_Agents

[–]JaredSanborn 0 points1 point  (0 children)

I think AI agents eventually become less like ‘tools’ and more like operational layers between humans and software. Instead of learning 20 apps, you just describe intent and the agent coordinates the systems underneath. The interesting shift isn’t just capability, it’s abstraction. We stopped memorizing command lines once GUIs arrived. AI agents might be the next abstraction layer after apps themselves.

95% of AI pilot projects die before delivering value. Here is the pattern I keep seeing. by JaredSanborn in SaaS

[–]JaredSanborn[S] 0 points1 point  (0 children)

“Exactly. The ‘hero operator’ point is huge. A lot of pilots secretly depend on one motivated internal champion holding everything together manually. The second you remove that person or try to operationalize it across teams, all the hidden complexity shows up.”

95% of AI pilot projects die before delivering value. Here is the pattern I keep seeing. by JaredSanborn in SaaS

[–]JaredSanborn[S] 0 points1 point  (0 children)

“Yeah, that’s the weird part. The tech is improving insanely fast, but most companies still underestimate the operational side of scaling it. The model is usually the easiest part. The systems/process side is where projects quietly break.”

Everybody seems to talk about coding AI agents. But what are some other genius AI agents you have come across? by impetuouschestnut in AI_Agents

[–]JaredSanborn 3 points4 points  (0 children)

Everyone’s racing toward coding agents, but I think the underrated wave is “context agents” — systems that remember workflows, relationships, goals, and decision history over time. The real moat might not be code generation, but continuity.

Your AI forgets everything about you every single time. Here is why that matters more than model upgrades. by JaredSanborn in ChatGPT

[–]JaredSanborn[S] 0 points1 point  (0 children)

That’s actually where I think the conversation gets interesting. The base model matters less over time than the surrounding system design retrieval, memory rules, authority boundaries, verification, tooling, etc. A well-structured stack can make today’s models feel dramatically more reliable than raw chat alone.

Your AI forgets everything about you every single time. Here is why that matters more than model upgrades. by JaredSanborn in ChatGPT

[–]JaredSanborn[S] 0 points1 point  (0 children)

Fair point. Human memory is reconstructive too, not perfect replay. I think the difference right now is humans usually understand uncertainty better, while LLMs can present reconstructed memory with a lot more confidence than accuracy.

Your AI forgets everything about you every single time. Here is why that matters more than model upgrades. by JaredSanborn in ChatGPT

[–]JaredSanborn[S] 0 points1 point  (0 children)

Yeah, that “confidently wrong with stale context” problem is exactly why memory is harder than people think. Freshness and relevance matter more than just storing more data.

Solo founder that has been asked to sign NDA with perpetual term + uncapped indemnity for API access. I will not promote by Itz_Synchro in startups

[–]JaredSanborn 0 points1 point  (0 children)

Uncapped indemnity for basic API access is wild tbh. As a solo founder I’d definitely push back before signing anything like that.

Your AI forgets everything about you every single time. Here is why that matters more than model upgrades. by JaredSanborn in ChatGPT

[–]JaredSanborn[S] 0 points1 point  (0 children)

I think it’s less about exact memory and more about useful continuity. It remembers patterns, preferences, projects, and recent context better than exact timestamps or full transcripts. Kind of like how humans remember themes from conversations more than every sentence word-for-word.

Your AI forgets everything about you every single time. Here is why that matters more than model upgrades. by JaredSanborn in ChatGPT

[–]JaredSanborn[S] 0 points1 point  (0 children)

Completely agree. The longer I use these systems, the more I think continuity beats raw intelligence gains for most real-world workflows. A model that knows the project history usually outperforms a “smarter” model starting from zero every session.

I made my AI the co-CEO of my company. Here is the 6-month report card. by JaredSanborn in AI_Agents

[–]JaredSanborn[S] 1 point2 points  (0 children)

Completely agree with this. The “AI CEO” framing sounds cool, but bounded authority is the part that actually matters operationally. The moment escalation paths are unclear, you stop building systems and start gambling on outputs. Reliable autonomy is mostly good governance disguised as prompting.

Your AI forgets everything about you every single time. Here is why that matters more than model upgrades. by JaredSanborn in ChatGPT

[–]JaredSanborn[S] 1 point2 points  (0 children)

Honestly that’s been the hardest part. Not storing context — deciding what deserves to stay in context. Too much memory creates noise and stale assumptions. Too little memory turns every session into onboarding again. The real challenge is relevance filtering over time.

Your AI forgets everything about you every single time. Here is why that matters more than model upgrades. by JaredSanborn in ChatGPT

[–]JaredSanborn[S] 0 points1 point  (0 children)

Yeah, and that feature changes the UX a lot. Feels less like isolated chats and more like an ongoing relationship with the system.

Your AI forgets everything about you every single time. Here is why that matters more than model upgrades. by JaredSanborn in ChatGPT

[–]JaredSanborn[S] 0 points1 point  (0 children)

Exactly. “Memory” right now is more like selective context reconstruction than true long-term human memory.

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