AI Software Engineer for On-Chain Economies
AI Software Engineer for On-Chain Economies
AI Software Engineer for On-Chain Economies
Crypto has a developer problem. Despite billions in funding, we're still waiting for the application wave that will bring blockchain to mainstream adoption. The bottleneck isn't infrastructure anymore—it's the shortage of developers who can build at the intersection of complex protocols, economic mechanisms, and user experience.
OpenBlock is solving this with the first AI software engineer built specifically for blockchain development. We're ending crypto's developer dearth and unlocking the application wave we've all been waiting for.
Battle-Tested Infrastructure Powering $2B+ in Protocol Simulation
Our journey began by building the data infrastructure that powered large-scale protocol simulation for blockchain protocols. We designed and implemented comprehensive simulation frameworks across ecosystems like zkSync, Sui, and Starknet, as well as protocol modeling systems for Optimism, Sonic, and Linea—collectively monitoring over $2 billion in capital flows through sophisticated optimization algorithms.
Supporting several billion-dollar protocol simulations at once demands a pipeline that is both comprehensive and fast. Our end-to-end stack handles the entire journey from raw block to actionable metric:
- Raw chain capture: We ingest blocks, logs, and traces from every major L1 and L2 within minutes of finality
- Structured warehousing: A dbt layer that decodes and transforms this into organized, high signal schemas (TVL, volume, deposits, etc.), enabling lighting-fast queries.
- Purpose-built Models: Proprietary models flag wash trading, measure economic efficiency, predict TVL, and analyze user stickiness - signals that feed directly into capital allocation algorithms.
- Simulation and alerting: Chain-replay jobs stress-test new reward weights and emission curves before deployment, while real-time dashboards surface volatility exceptions within minutes for operations teams and foundations.
- On-demand dashoarding: A self-serve interface lets non-engineers build custom charts on the spot, so growth, product, and marketing teams can explore fresh data without waiting for dev cycles.
This stack became the foundation for every protocol simulation we deploy. But more importantly, simulating $2+ billion in real capital flows taught us something crucial: we had built the perfect training ground for blockchain agents.
Blockchain: The Ultimate Training Ground for AI
Blockchain provides what scientific domains offer for AI training: naturally verifiable rewards. Just as the physical world is the ultimate arbiter of accuracy in science, economic outcomes are the ultimate arbiter of performance in blockchain. Our data infrastructure modeling 15+ blockchains captures this objective feedback at unprecedented scale.
Every protocol mechanic, every user behavior pattern, every edge case we encountered simulating $2+ billion in capital flows provides unfakeable training signals. Unlike academic labs that build toy environments with synthetic rewards that can be gamed, blockchain offers cryptographically verified economic outcomes—profit, loss, gas efficiency, user retention—that create natural selection pressure for superior models.
This isn't just our competitive advantage—it's blockchain's inherent superiority as a training domain. When our agents optimize gas usage or design incentive mechanisms, they've learned from objective market feedback that cannot be manipulated. The result: reasoning models that excel in adversarial, high-stakes environments because economic reality was their teacher.
The Perfect Training Environment for Agents
Once the data backbone was in place and customer demand grew, we began replacing entire job functions with specialised agents.
- DE-AI is our autonomous data engineer. It ingest raw, decoded logs, classifies them, and writes the ETL code to ingest data into our defined schemas without human help. This allows us to ingest and store new protocol data at rapid speeds at the fraction of the cost of hiring more DE/DAs.
- OB-1 was trained as a frontier text-to-SQL reasoning model using real analytical tasks where accuracy was measured against actual protocol outcomes, not human preferences. Operating as a 24/7 data science team, it writes SQL with higher accuracy than commercial products. OB-1 already publishes the weekly Optimism SuperStacks report read by thousands of users and is live on 8 chains.
- Text-to-Transaction closes the loop. If a user types, “Allocate next week’s emissions seventy percent ETH, thirty percent wstETH,” the agent parses intent, assembles the call graph, simulates execution, and then executes the transaction once the user has signed off.
Together these agents create a continuously improving flywheel: block data becomes metrics, metrics become insight, insight becomes on-chain action.
Real World Examples
Major ecosystems have already adopted our agents:
Optimism uses OB-1 for their weekly SuperStacks analytics reports, reaching thousands of readers and informing ecosystem decisions with automated insights
zkSync, Sui, and Starknet protocols have benefited from our infrastructure managing their incentive programs, with our agents autonomously adjusting emissions based on real-time protocol health metrics.
Enterprise Partners use DE-AI to onboard new protocols in hours rather than weeks, with autonomous data pipeline generation that rivals senior blockchain engineers.
Unlocking the Application Wave: The Full-Stack Blockchain SWE
Our specialized agents were just the beginning. The reinforcement learning gyms we've built to train these agents are now being used to create the industry's next frontier: a complete blockchain software engineer.
This AI SWE will draft smart contracts, simulate them against historical data, optimize gas usage, and generate governance proposals—all autonomously. For the first time, developers won't need to be experts in protocol mechanics, economic design, and low-level blockchain optimization to build breakthrough applications.
The result? We'll finally unlock the application wave that brings blockchain to mainstream adoption. The first ecosystems to adopt this workflow will set the standard for a new paradigm in blockchain development.
Current Capabilities and Limitations
Our agents excel at well-defined blockchain tasks but have limitations. They work best with established protocols and clear documentation, and currently require human oversight for complex governance decisions. As AI capabilities advance, we anticipate our agents handling more sophisticated protocol design and cross-chain interactions autonomously.
Try our live agents at obl.dev to experience autonomous blockchain development firsthand.
Call To Action
If you are an ecosystem that wants to usher in a new era of blockchain development, talk to us. OpenBlock Labs is converting years of hands-on protocol work and data engineering to empower blockchain developers and ecosystems that are ready to code and run the next generation of blockchain.
AI Software Engineer for On-Chain Economies
Crypto has a developer problem. Despite billions in funding, we're still waiting for the application wave that will bring blockchain to mainstream adoption. The bottleneck isn't infrastructure anymore—it's the shortage of developers who can build at the intersection of complex protocols, economic mechanisms, and user experience.
OpenBlock is solving this with the first AI software engineer built specifically for blockchain development. We're ending crypto's developer dearth and unlocking the application wave we've all been waiting for.
Battle-Tested Infrastructure Powering $2B+ in Protocol Simulation
Our journey began by building the data infrastructure that powered large-scale protocol simulation for blockchain protocols. We designed and implemented comprehensive simulation frameworks across ecosystems like zkSync, Sui, and Starknet, as well as protocol modeling systems for Optimism, Sonic, and Linea—collectively monitoring over $2 billion in capital flows through sophisticated optimization algorithms.
Supporting several billion-dollar protocol simulations at once demands a pipeline that is both comprehensive and fast. Our end-to-end stack handles the entire journey from raw block to actionable metric:
- Raw chain capture: We ingest blocks, logs, and traces from every major L1 and L2 within minutes of finality
- Structured warehousing: A dbt layer that decodes and transforms this into organized, high signal schemas (TVL, volume, deposits, etc.), enabling lighting-fast queries.
- Purpose-built Models: Proprietary models flag wash trading, measure economic efficiency, predict TVL, and analyze user stickiness - signals that feed directly into capital allocation algorithms.
- Simulation and alerting: Chain-replay jobs stress-test new reward weights and emission curves before deployment, while real-time dashboards surface volatility exceptions within minutes for operations teams and foundations.
- On-demand dashoarding: A self-serve interface lets non-engineers build custom charts on the spot, so growth, product, and marketing teams can explore fresh data without waiting for dev cycles.
This stack became the foundation for every protocol simulation we deploy. But more importantly, simulating $2+ billion in real capital flows taught us something crucial: we had built the perfect training ground for blockchain agents.
Blockchain: The Ultimate Training Ground for AI
Blockchain provides what scientific domains offer for AI training: naturally verifiable rewards. Just as the physical world is the ultimate arbiter of accuracy in science, economic outcomes are the ultimate arbiter of performance in blockchain. Our data infrastructure modeling 15+ blockchains captures this objective feedback at unprecedented scale.
Every protocol mechanic, every user behavior pattern, every edge case we encountered simulating $2+ billion in capital flows provides unfakeable training signals. Unlike academic labs that build toy environments with synthetic rewards that can be gamed, blockchain offers cryptographically verified economic outcomes—profit, loss, gas efficiency, user retention—that create natural selection pressure for superior models.
This isn't just our competitive advantage—it's blockchain's inherent superiority as a training domain. When our agents optimize gas usage or design incentive mechanisms, they've learned from objective market feedback that cannot be manipulated. The result: reasoning models that excel in adversarial, high-stakes environments because economic reality was their teacher.
The Perfect Training Environment for Agents
Once the data backbone was in place and customer demand grew, we began replacing entire job functions with specialised agents.
- DE-AI is our autonomous data engineer. It ingest raw, decoded logs, classifies them, and writes the ETL code to ingest data into our defined schemas without human help. This allows us to ingest and store new protocol data at rapid speeds at the fraction of the cost of hiring more DE/DAs.
- OB-1 was trained as a frontier text-to-SQL reasoning model using real analytical tasks where accuracy was measured against actual protocol outcomes, not human preferences. Operating as a 24/7 data science team, it writes SQL with higher accuracy than commercial products. OB-1 already publishes the weekly Optimism SuperStacks report read by thousands of users and is live on 8 chains.
- Text-to-Transaction closes the loop. If a user types, “Allocate next week’s emissions seventy percent ETH, thirty percent wstETH,” the agent parses intent, assembles the call graph, simulates execution, and then executes the transaction once the user has signed off.
Together these agents create a continuously improving flywheel: block data becomes metrics, metrics become insight, insight becomes on-chain action.
Real World Examples
Major ecosystems have already adopted our agents:
Optimism uses OB-1 for their weekly SuperStacks analytics reports, reaching thousands of readers and informing ecosystem decisions with automated insights
zkSync, Sui, and Starknet protocols have benefited from our infrastructure managing their incentive programs, with our agents autonomously adjusting emissions based on real-time protocol health metrics.
Enterprise Partners use DE-AI to onboard new protocols in hours rather than weeks, with autonomous data pipeline generation that rivals senior blockchain engineers.
Unlocking the Application Wave: The Full-Stack Blockchain SWE
Our specialized agents were just the beginning. The reinforcement learning gyms we've built to train these agents are now being used to create the industry's next frontier: a complete blockchain software engineer.
This AI SWE will draft smart contracts, simulate them against historical data, optimize gas usage, and generate governance proposals—all autonomously. For the first time, developers won't need to be experts in protocol mechanics, economic design, and low-level blockchain optimization to build breakthrough applications.
The result? We'll finally unlock the application wave that brings blockchain to mainstream adoption. The first ecosystems to adopt this workflow will set the standard for a new paradigm in blockchain development.
Current Capabilities and Limitations
Our agents excel at well-defined blockchain tasks but have limitations. They work best with established protocols and clear documentation, and currently require human oversight for complex governance decisions. As AI capabilities advance, we anticipate our agents handling more sophisticated protocol design and cross-chain interactions autonomously.
Try our live agents at obl.dev to experience autonomous blockchain development firsthand.
Call To Action
If you are an ecosystem that wants to usher in a new era of blockchain development, talk to us. OpenBlock Labs is converting years of hands-on protocol work and data engineering to empower blockchain developers and ecosystems that are ready to code and run the next generation of blockchain.
