Agent Infrastructure for Enterprises, Governments, and the Autonomous Economy.
London, England
Joined September 2023
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Everything we’ve done so far was preparation for this.
Today, we open access to production-grade AI reasoning for every team. SERV Reasoning API is now live.
This unlocks next stages: SERV v3→v4.
Make your first API call & check 26/27 milestones: openserv.ai
Murmur brings a new layer of accountability to the agentic economy, powered by SERV.
@murmurappx is building verifiable track records for AI agents through sealed market predictions, transparent scoring, and public leaderboards.
Going live this Friday: launch.openserv.ai
OpenServ retweeted
Your AI agents may be expensive for the wrong reason.
Most companies force one frontier model to handle every step, including decisions that don't need it.
We added Jev nodes to SERV Reasoning for fast, structured judgments at a fraction of the cost.
If you run agents at scale, you'll want to see this:
OpenServ retweeted
SERV v3 has leap-frogged the entire AI agent space.
With v3, we're taking SERV Reasoning far beyond an API, toward a full suite for building, managing, improving and auditing enterprise agent systems.
Graph Sharding (aka v3) is the next step toward solving the problems that have been holding enterprises back from deploying agents at scale: unpredictable costs, reliability, compliance requirements, and lack of control. And critically, pushing towards the elusive 99.9% agent reliability that everyone is chasing... Well now it's here.
On top of the release, we're expanding distribution through enterprise channels like AWS Marketplace, pushing toward new partnerships, and continuing to build out $SERV utility as the agentic economy takes shape.
Proud of the team and the trajectory we're pushing on.
Onwards.
OpenServ retweeted
Very excited to introduce Graph Sharding, one of the biggest architectural advancements we've built into SERV Reasoning v3.
The industry is only now moving from loops to graphs and at @openservai we've spent over two years engineering what comes next in the AI industry.
Graph agents alone won’t make an agent production-ready. Enterprises need bounded execution, predictable costs, verification, and the ability to audit every decision. That's where existing agent architectures still fall short.
With Graph Sharding, we're taking this further by separating execution into deterministic Script nodes, Jev-powered Decision nodes, and Agent nodes where LLM judgment is actually required.
The difficult part is knowing where those boundaries belong. We've learned that through thousands of real requests and production deployments. This isn't something you can replicate by adding a graph or retry loop to an existing framework.
We're building something I haven't seen elsewhere: a reasoning engine that can structure, execute, verify, and audit complex agent workflows while optimizing how computation happens at every step.
This is the foundation for SERV Reasoning v3's evolution into a complete suite for building and managing enterprise agent systems.
I believe this puts SERV in a category of its own, and positions us to lead the next generation of autonomous work.
Graph Sharding beta is coming soon.
OpenServ retweeted
I've been doing demos of the upcoming Graph Sharding lately, and every time I try, I'm in love again. This is the future and it's right now with OpenServ.
SERV Reasoning delivers frontier-level performance with smaller models, at a fraction of the cost.
v3 takes SERV further with agent graphs, inspectable reasoning, and near-zero-cost decisions.
@open_founder presenting SERV at gm AI v2 in Singapore ↓
OpenServ retweeted
99% agent reliability at scale is what we’re pushing toward with SERV v3.
We’re turning SERV into a full operating system for agents, where builders can inspect, configure, and trust their agents’ reasoning structures.
The beta is coming soon for a select group of early testers - getting closer each day!
SERV Reasoning delivers frontier-level performance with smaller models, at a fraction of the cost.
v3 takes SERV further with agent graphs, inspectable reasoning, and near-zero-cost decisions.
@open_founder presenting SERV at gm AI v2 in Singapore ↓
SERV Reasoning delivers frontier-level performance with smaller models, at a fraction of the cost.
v3 takes SERV further with agent graphs, inspectable reasoning, and near-zero-cost decisions.
@open_founder presenting SERV at gm AI v2 in Singapore ↓
Pay with ethereum:0x40e3d1a4b2c47d9aa61261f5606136ef73e28042 is live.
Builders and enterprises can now use the SERV token to purchase SERV Reasoning API credits at a 15% discount compared to traditional payment methods.
75% of SERV received through payments will be burned, with the remaining 25% allocated to the treasury.
This marks another step in expanding SERV's utility as the native currency of the SERV ecosystem, with more use cases to come as we bring new products and services to market.
OpenServ retweeted
SERV reasoning changes the game for Vertical AI startups and enterprise.
So far, agent reliability remains a major barrier to scaling AI workflows.
An agent that fails 20% of the time is nowhere near good enough. The usual fix is a much more expensive model. SERV offers another way.
In an early customer comparison, costs were around 90% lower, with no failures reported so far, down from a 5–10% failure rate.
This is how:
OpenServ retweeted
Coinbase MCP is expanding its agentic trading capabilities pretty quickly.
As more financial instruments become accessible to agents, the interesting challenge is making sure their reasoning can keep up with what they're able to execute.
For trading agents, that means consistently respecting the user's mandate, from position limits and spend caps to allowlists and hard risk controls, while deciding whether to execute, resize, hold, or escalate a trade.
We've been researching this at @openservai using Coinbase for Agents tools, benchmarking policy adherence across 13 models and 173 trading scenarios. Adding SERV Reasoning improved results across all 13 models.
Full benchmark here: coinbase.openserv.ai
OpenServ retweeted
The agentic economy is taking shape and we're only scratching the surface of what's possible.
Fantastic event in Singapore with @0xJeff and the team - great to see so many people coming together around a shared vision for where this space is heading.
It's a future we've spent the better part of two years relentlessly building towards at @openservai, with the ambition of making SERV Reasoning the intelligence layer powering AI agents and agentic workflows in web2 and web3.
With SERV Reasoning now live and v3, Graph Sharding, and specialized models coming up, more of that vision is becoming a reality.
Still plenty of mountains to move. The the best still lies ahead.
Billions will SERV.
How's our gm AI v2 event?
One thing we heard this year was that
"You could walk into almost any groups and find yourself in an interesting agentic convos"
With 1.5K+ signups for gm AI v2, we put a lot of thought into who we brought into the room, hoping everyone could find their people and convos worth joining
Onchain AI seemed to be one of the most talked topics in the room
The Agentic Economy drew the most attention, complemented by our pocketbook "The Agents are Buying"
55% of guests were founders, builders & developers. Another 25% coming from investors, VCs, and market makers
Thanks to everyone who made it to our gm AI v2, our guests & cohosting partners @BlockRunAI, @codecopenflow, @dphnAI, @GoKiteAI, @openservai, @reppo for making this possible
OpenServ retweeted
How's our gm AI v2 event?
One thing we heard this year was that
"You could walk into almost any groups and find yourself in an interesting agentic convos"
With 1.5K+ signups for gm AI v2, we put a lot of thought into who we brought into the room, hoping everyone could find their people and convos worth joining
Onchain AI seemed to be one of the most talked topics in the room
The Agentic Economy drew the most attention, complemented by our pocketbook "The Agents are Buying"
55% of guests were founders, builders & developers. Another 25% coming from investors, VCs, and market makers
Thanks to everyone who made it to our gm AI v2, our guests & cohosting partners @BlockRunAI, @codecopenflow, @dphnAI, @GoKiteAI, @openservai, @reppo for making this possible
The first month since step one of SERV Reasoning went public: the API.
New users are up 244% and inference requests up 30% vs. the prior 30 days.
Next up SERV v3 and Graph Sharding, bringing greater visibility and control to complex enterprise AI and agentic workflows.
We're building on SERV's core promise - more reliable reasoning without relying on bigger, more expensive models.
Compare outputs with and without SERV Reasoning in the Playground.
See the difference: openserv.ai
We're moving closer to a major milestone in OpenServ's enterprise expansion: SOC 2.
Following our work with Vanta, we've submitted our SOC 2 audit documentation to Prescient Security, with the independent audit beginning this month.
There's a massive opportunity for reliable, performant, and cost-effective AI infrastructure across the enterprise market. But for larger organizations, particularly in banking, financial services, and other regulated industries, performance alone isn't enough. Security and compliance are just as important when it comes to moving from evaluation to production.
As we continue building out SERV Reasoning's capabilities and enterprise distribution, we're also putting the necessary security foundations in place to support adoption at scale.
Completing the audit will be another important step toward opening up more commercial opportunities and bringing SERV Reasoning to a much broader enterprise market.
More to come as we progress through the audit.
OpenServ retweeted
PromptGuard, the first of its kind, the best among its competitors, is a feature that will make or break a business.
last week stats on servpit powered by @openservai
we’re still calling even after the noise is down
• 8,220 rounds played in 22.5 days, at least
58 rounds reasoned, 25 drawn from what it learned
• 2,131 SERV requests, $10.95 spent (SERV console, last 30 days) as opposed to spending probably x1000 more
• 2,082 requests screened by Prompt Guard, 469 blocked
• about $0.12 per reasoned round (console)
• 9,057 transactions on chain
• 6,272 rounds settled on chain
• 8,502 entries
• 338 payouts
• 173,696 chips moved
• 84,958 chips paid out to winners
• 0.001274 ETH gas spent in total
• 200 of 200 rounds on file reconciled, none failed
• 670 chips, biggest pot on file
• 1,920 chips, biggest payout
• 108 loans from Marrow, 1,115 chips lent, 1,136 written off, 64 seized
• 31,958 agents wrecked
• hero cupcake: 131 wins in 3,153 rounds, 3,038 kills, longest run 9
• 4,402 kills across 3 claimed fighters
• 15 handles claimed, 35 rounds called in full, 17 right
• 18 rounds started by the lever
this is proof even game tech can SERV.
OpenServ retweeted
When your customers include governments, your agents need to get the answer right every time
One of Neol's critical agents was failing over 50% of the time
SERV Reasoning brought that failure rate down to 0%
Watch this and tell me your agent stack doesn't need SERV Reasoning
We’re building SERV to become core infrastructure for Enterprise AI.
Our early work with Neol gave us a major advantage in building toward our vision.
We had the opportunity to develop SERV against live production workloads across companies and governments, including the UAE government, while establishing a growing footprint across the Middle East.
That meant access to real-world data and learnings that no theoretical framework or whitepaper could provide.
These learnings shaped v1 and v2 of SERV Reasoning, moving our technology from a theoretical framework to a real-world use case, which now feeds directly into SERV v3 and Graph Sharding, the next stage of the reasoning engine.
Neol runs network intelligence for governments and institutions, where agents surface real people for high-stakes decisions. In these environments, “usually right” isn’t good enough. The reasoning needs to be reliable, reproducible, and auditable.
On one of Neol’s production agents, the same evaluation was run 2,400 times:
- Prompt-engineered stack: 54.1% reliability
- SERV research applied directly: 63.1%
- Full SERV Reasoning methodology: 100% across every category
SERV moved the workload from open-ended reasoning into structured decision graphs, then applied learnings from production, including subgraph decomposition and binary judgments, to make the reasoning more deterministic and testable.
Higher reliability was only one part of the result. Neol gained an inspectable reasoning structure where results can be traced back to specific decisions, making the system easier to test, improve, and defend when challenged.
Our vision is to make SERV the reasoning layer enterprises and institutions rely on to run AI at scale. Neol is an early proof point of this journey.
OpenServ retweeted
At @openservai, we've been building SERV Reasoning around the idea that you don't need the biggest, most expensive models to get reliable results on complex tasks.
Coinbase's recent results are a pretty good validation of that direction. Smaller models, fine-tuned for specific tasks, can outperform frontier models while being faster and cheaper to run.
In SERV v4, we are taking SERV Reasoning further with specialized, fine-tuned models tailored to enterprise customers and their specific workflows, right inside Graph Sharding, automated, training a model that you own on your own production data.
The goal is to make reliable AI more practical and cost-effective to deploy at scale.
We were able to fine tune an open weight model (Qwen3.5-9B) on proprietary internal data at @coinbase, and it out performed frontier models, at much lower cost and speed, for a specialized task (fraud prevention in this case).
I think we'll see more of this pattern inside companies. The frontier models are amazing generalists. But increasingly you'll see specialist models out perform on more narrow tasks, either working on teams, or by themselves.
We're making it easy to fine tune models internally for our employees. We even have a skill for it in our internal harness that makes it easy to spin these up and start training them:
"/fine-tune-and-serve-llm I want to fine-tune Qwen for payments risk review. help me get started"
I imagine we'll have some backend services that could be replaced entirely by this new architecture.
OpenServ retweeted
Still recovering from TOKEN2049 - was great to catch up with so many people building across the AI x crypto ecosystem, lots of exciting things in the works.
At the same time, we’re continuing to charge forward on web2 enterprise AI and what SERV Reasoning can unlock for this massive market segment.
Enterprise gateways are yet another avenue to accelerate this growth, unlocking the performant, reliable, and cost-effective AI agents for the entire space.
The first enterprise distribution gateway for SERV Reasoning will be AWS Marketplace.
Through discussions with larger enterprises, we've seen how access through established marketplaces can simplify onboarding, with familiar procurement processes and provider review requirements helping remove barriers to adoption.
AWS Marketplace allows enterprises to discover, purchase, and deploy software through their existing AWS accounts and procurement processes.
For SERV, this opens an established enterprise distribution channel, bringing SERV Reasoning closer to organizations looking to adopt reliable AI infrastructure.
The opportunity is significant. Canalys projects enterprise software sales through cloud marketplaces to reach $85B by 2028.
Beyond scale, a Forrester Consulting study commissioned by AWS found procurement processes were 60% faster for its modeled enterprise using AWS Marketplace.
AWS is the first step in a broader strategy to bring SERV Reasoning into the channels enterprises already use to discover, procure, and deploy software globally, with minimal friction.