AI agents dominated the first quarter of this year. You didn’t need to look far for startups, funding, and general excitement pouring into this sector. These everything assistants are capturing the public imagination by promising to revolutionize processes, efficiencies, and decision-making. At home and work, agents will become our best friends.
But there’s a problem. Accurate insights require accurate information and, unfortunately, data pipelines currently draw from public and private sources without proper compensation or attribution. This copyright headache only deepens mistrust and misunderstanding of these tools. This matters since, without trust to welcome agents into our most important personal and professional spaces, they won’t access the qualitative information required for best results.
If agents are only as “smart” as their data, our sector must ensure backend infrastructure that inspires confidence and acknowledges data owners. Clearly, more than computational power, mainstream adoption and artificial general intelligence need quality data.
The agentic data problem
So, what’s powering these platforms, anyway? Most generative AI platforms pull from a patchwork of siloed, unverified, or public sources. As a result, data owners aren’t paid for their insights nor do users know their provenance. This one-two punch of unknown and unattributed sourcing hurts both trust and output. This just isn’t good enough for the coming wave of agents.
I like to use the example of a trading bot. To understand the ins and outs of its function, this agent not only needs historical data and real-time access but also geopolitical, economic, and contextual understanding. The agent’s decision-making suffers if this information is inadequately verified or scattered across the digital ether. Our sector is aiming for agents that can independently analyze, adapt, and act with clarity and autonomy. This simply isn’t possible if the data coming in is inaccurate, unreliable, and general rather than specific.
Agents and their backends require more than raw processing power to truly transcend the boundaries of isolated intelligence. Instead, they need access to meaningful, secure, and actionable data. The good news is that this is possible with the transparency and traceability of blockchain.
The blockchain architecture solution
Blockchain-backed infrastructure empowers agents with access to high-quality data on-chain and off-chain, enabling them to make better-informed, autonomous decisions in decentralized ecosystems. This is a big step because it creates data guardrails by letting agents know what they can and can’t access. Along with enhanced metrics and tags, real-time insights and cross-chain data integration, agents become context-aware and armed with the most up-to-date and qualitative information.
It’s also vital for agents to understand where data comes from and who owns it. In this sense, web3 solutions show promise. CARV ID lets users centralize their online presence by uniting multiple profiles under one banner. As a result, they can choose whether or not to share this information with agents. If they do, there are built-in mechanisms to acknowledge ownership and bring them in on the value created while delivering agents with zero-party and regulatory-compliant data. Story Protocol is
Of course, if users are going to share their information, they also need guarantees that it will stay safe. Again, digital guardrails engender trust with things like zero-knowledge proofs and Trusted Execution Environments (TEE).
Agents and users alike require a holistic solution that bakes data respect into the foundations. Blockchain makes this happen and delivers much-needed trust at an agentic inflection point.
Why this matters for developers, researchers and users
Better data gives better results for all participants in AI. For developers, the agentic infrastructure above simplifies the creation of smarter, autonomous agents with built-in tools for accessing and processing enriched data. Agents can therefore evolve collaboratively within a decentralized ecosystem, much like humans evolving through shared experiences and adaptation.
For researchers, addressing data fragmentation, inefficiency, and lack of trust is vital. Defining the infrastructure and protecting the data helps push the boundaries of what intelligent systems can achieve and unlock new avenues for collaboration and discovery.
And last but not least, for users, they benefit from access to high-quality, actionable insights tailored to their needs without compromising privacy. Further, they can finally share in the value created from their information, a far cry from the data status quo of web2.
We can no longer and should no longer accept AI technology as a “black box” where information goes in and commands come out. All stakeholders deserve better. Forget computing and processing power – ushering in the agentic age starts by acknowledging AI’s faults and baking in better infrastructure and quality data.
