AI agents are simple to describe and complex to serve: observe → decide → act 
→ learn. Each loop depends on fresh, reliable, permissionless data. In Web2, 
you can rent this from a few platforms. In Web3, data lives across dozens of 
heterogeneous chains, node stacks, indexers, and off-chain oracles &#8211; each with 
its own quirks of latency, finality, semantics, and failure modes. The result: 
agents are hungry; the pantry is chaotic. Let&#8217;s understand the problem, public 
signals, and outline what an AI-ready data layer must look like to unlock the 
agentic economy for DeFi and beyond. AI is rapidly penetrating Web3, but the 
bottleneck remains data. Prominent builders are increasingly agreeing that AI 
and crypto are complementary: AI brings generative capability and autonomy, 
while crypto brings ownership, provenance, and open markets for compute and 
data. Chris Dixon has argued that AI systems need blockchain-enabled computing 
to reopen the internet and align incentives for data and model access. Vitalik 
Buterin categorizes crypto×AI touchpoints: AI as interface, player, target of 
economic guarantees and stresses careful incentive design, i.e., you can&#8217;t 
bolt AI onto adversarial markets without thinking through data quality and 
safety. On the execution side, DeFi itself is moving towards intent-based 
designs (i.e., you state an outcome; solvers compete to fulfil it), precisely 
because raw, on-chain data flows are hostile to good UX under latency and MEV. 
Uniswap Labs and Across proposed ERC-7683 , a cross-chain intents standard, as 
a shared rail for this pattern. Takeaway: agents are arriving; markets are 
adapting; data remains the constraint. The Ugly Truth: What AI developers in 
Web3 run into Heterogeneity. Every chain has its own RPC behaviour, logs, 
event schemas, reorg patterns, and finality assumptions. Basic queries (e.g., 
&#8220;positions across Base+Solana+Polygon&#8221;) turn into N bespoke indexers. 
Staleness vs. cost. You can get cheap, slow data, or fast, expensive data 
(custom stream indexers, managed mirrors). Choosing both is nontrivial. 
Semantics. Blocks are facts; insights are models. Converting logs into 
entities (pools, positions, P&amp;L) involves constant ETL and re-computation, per 
protocol and per chain. Reliability under load. Network congestion and oracle 
lag create precisely the tail risks that autonomous agents are least able to 
mask. Indexing providers and docs agree on the fundamentals: direct chain 
queries are complex and slow; you need subgraphs or equivalent mirrors for 
performance, then you still must solve cross-chain streaming and schema 
normalization. &#8220;Actionable data&#8221; defined and why Web3 is short of it Call data 
is actionable when an agent can decide and execute within a bounded jitter 
budget while preserving correctness. Concretely: Normalized semantics: tokens, 
pools, positions, transfers, prices with consistent types/units across chains. 
Freshness &amp; determinism: p95/p99 latency SLOs, plus finality-aware freshness 
(soft vs. brutal finality). Verifiability: cryptographic provenance or 
replayable derivation (subgraph versions, mirror checksums). Compute-near- 
data: scoring, anomaly detection, route simulation, co-located with the 
streams. Streaming + time-travel: append-only event streams plus indexed 
snapshots for &#8220;what changed?&#8221; queries. Today&#8217;s Web3 stack gives you fragments 
of this (subgraphs, RPCs, analytics APIs), but not the cohesive, cross-chain, 
low-latency fabric that production agents demand. Even The Graph&#8217;s own 
materials and third-party guides frame direct chain access as complex, pushing 
developers to indexing/mirroring systems for practicality. Lessons from real 
incidents: when latency and fragmentation bite Here are a few recent AI×Web3 
products that have closed, been shelved, or effectively ceased operating : 
Planet Mojo’s “WWA” platform for AI gaming agents: shut down on July 1, 2025 
alongside the studio’s flagship game Mojo Melee, citing shifting market 
realities. Brian (AI → onchain transaction builder) : a Web3 “text-to- 
transaction” assistant that started at ETHPrague 2023; the team announced 
termination of operations on May 26, 2025 after losing first-mover advantage 
as agentic executors proliferated. TradeAI / Stakx (AI-trading schemes using 
NFTs &amp; “algos”) : took in hundreds of millions, then froze withdrawals and 
stopped operating; now the subject of a U.S. class-action lawsuit alleging 
unregistered securities and misrepresentations. (A clear cautionary tale of 
“AI” claims in crypto.) BitAI (“hands-free” AI crypto autotrader) : went 
offline in March 2024 after promising AI automated profits; Regulatory halts 
intersecting AI &amp; Web3: While not a permanent failure, Worldcoin (World 
Network) saw operations temporarily suspended in Indonesia in May 2025, 
illustrating how compliance risk can abruptly derail AI-adjacent Web3 
rollouts. Patterns we observed Latency + data fragmentation kills agents in 
production. Teams that promised “natural-language to onchain” often struggled 
with multichain freshness/finality and brittle indexing, leading to misses or 
costly infra band-aids. Hype-to-ROI gap: Analyst firms expect a high 
cancellation rate for “agentic AI” projects over the next couple of years- 
costs, unclear value, and risk controls are the common failure modes. “AI 
trading” claims = red flag category. Regulators and watchdogs repeatedly flag 
“proprietary AI bot” pitches as high-risk; many go dark or morph after a 
marketing blitz. &#8220;Data fragmentation is the biggest barrier for AI agents in 
Web3: too many chains, schemas, and brittle APIs force agents to choose 
between stale signals or endless stitching. Latency, freshness gaps, and 
complex on-chain execution turn good strategies into missed trades, while 
inconsistent formats cause grounding errors, model drift, and brittle 
behavior. The solution is a unified, real-time semantic data layer with 
normalized schemas, streaming indexers, canonical events, and deterministic 
fallbacks, so agents focus on strategy, not plumbing. At Elsa, we’re building 
this agentic layer with cross-chain liquidity, data endpoints, and real-time 
RAG (WIP), turning fragmented chaos into reliable autonomous execution.&#8221; 
-Dhawal Shah, Founder and CEO at HeyElsa Patterns that work: solutions around 
today&#8217;s incapabilities Intent rails, not raw calls. Shift from &#8220;do X at 
address Y&#8221; to &#8220;achieve outcome Z,&#8221; then let solvers compete, hedging 
MEV/latency at the meta-layer Finality-aware freshness. Expose &#8220;freshness + 
confidence&#8221; to agents (e.g., soft finality at N confirmations vs. brutal 
finality after epoch), so policies can adapt. Compute-to-data. Move 
scoring/simulation to the stream edge to avoid fan-out latency. Proofs &amp; 
fallbacks. Two independent sources for critical signals (e.g., price) plus 
explainable derivations to help agents learn from misses. Human-in-the-loop 
gates. For high-impact actions, require explicit sign-off or bounded policy 
budgets. NewsBTC analyzed major intent rails and indexing providers, and 
gathered insights on today’s challenges from a recently launched AI×Web3 
product. &#8220;AI agents don’t fail on logic, they fail on inputs. Blockchains emit 
raw, inconsistent log fragments without context. Until we have a neutral layer 
that normalises and verifies this data in real time, agents in Web3 are 
operating blind. The challenge isn’t building more intelligent AI. It’s giving 
them clean, reliable signals to act on.&#8221; -Nasim Akthar, CTO at Igris.bot What 
an AI-ready data layer should look like &#8211; spec, not hype Think of it as 
Programmable, Verifiable, Real-Time, Cross-Chain: Ingestion &amp; normalization: 
Multi-chain connectors → canonical schemas (tokens, pools, positions, prices, 
routes) with explicit units and decimals. Streaming + snapshots: Kafka-like 
streams for events; OLAP snapshots for time-travel and joins. Mirrors with 
provenance: Deterministic mirrors of subgraphs or equivalent, with versioned 
transforms and integrity checks so agents can reason about data lineage. On- 
stream compute: Built-ins for volatility, liquidity depth, route simulation, 
slippage/risk scores co-located with streams to meet p95 targets. Finality- 
aware freshness API: Every read returns : freshness_ms, confirmations, 
finality_level so policies can gate actions. Intent hooks: First-class 
bindings to intent rails (CoW, 7683, Across) so &#8220;decide → act&#8221; is one call, 
with simulation receipts, Safety &amp; audit: Rate limits, kill-switches, replay 
logs, and post-trade proofs for continuous learning. Future of AI × Web3: 
markets of agents, paying for provable data With the right data layer, the 
frontier expands: Agent MM &amp; risk: autonomous market-making that prices data 
freshness &amp; finality into quotes. Governance copilots: agents that read 
proposals, simulate outcomes, and stake opinions with cryptographic 
attestations. Cross-chain portfolio policies: &#8220;End with 2 ETH on Base if 
weekly variance &gt; X,&#8221; routed by intent rails under bounded latency. Data 
markets for models: provenance-aware datasets and inference services with on- 
chain payment &amp; usage proofs Safety layers: Vitalik&#8217;s caution stands &#8211; 
interfaces and policies must be designed to mitigate scams and misalignment. 
Build rails that bias toward correctness, not just speed. Closing: 
architecture is destiny If agents are the next user layer, your architecture 
becomes your product. Teams that continually patch RPC calls and cron ETLs 
will struggle to keep up with multi-chain, real-time, adversarial markets. 
Teams that stand up an AI-ready data layer &#8211; normalised, mirrored, computable, 
finality-aware, and wired to intent rails, will ship agents that observe, 
decide, act, and learn at production speed. Give agents the data fabric they 
deserve. They&#8217;re hungry, and the market won&#8217;t wait.

AI Agents Are Hungry; Web3 Data Is a Mess : Why an AI-Ready Data Layer Is the Need of the Hour

Ether’s exchange flux metric turned negative for the first time in history, 
signaling a shift in investor behavior and Ether’s potential to resume its 
uptrend.

Ethereum bull alert: ETH exchange ‘flux’ turns negative for the first time

The U.S. Securities and Exchange Commission (SEC) is reading a plan to keep 
Bitcoin, Ethereum, and other crypto safe if super-fast quantum computers show 
up one day. SEC Urged to Prepare for Cryptography Meltdown The 74-page Post- 
Quantum Financial Infrastructure Framework (PQFIF) plan was sent Sept. 3. It 
says today’s cryptography could break later when computers […]

Can the SEC Protect Bitcoin? Quantum Defense Plan Sparks Criticism

The debate over whether XRP could surpass Bitcoin has gained intensity in this 
cycle, and many analysts and commentators have weighed in on the possibility. 
A recent video posted on X by crypto analyst and commentator CryptoSensei 
touched on this discussion, where he made the bold claim that developments in 
interoperability, regulation, and tokenized real-world assets could eventually 
put XRP ahead of Bitcoin. The Pundit’s Claim: XRP In Front Of Bitcoin Although 
Bitcoin is currently the largest cryptocurrency, XRP’s positioning this cycle 
has increased discussions of a shift in dominance. Interestingly, XRP has 
overtaken many cryptocurrencies in the past few months and is now on the heels 
of Ethereum in terms of market cap. Related Reading: Real Vision CEO Raoul Pal 
Calls ‘Full Port’ Into XRP, Ethereum In his video, CryptoSensei focused on the 
broader trajectory of blockchain adoption over the next decade, which is going 
to include the integration of real-world assets like stocks, bonds, 
derivatives, and real estate into digital systems. He noted that only a small 
fraction of these markets are currently on-chain, and he predicted that this 
figure will perhaps rise just five to ten percent in the next ten years. The 
pace of this growth will be determined by global regulatory cooperation, where 
working groups from the G7 and G20 align laws to allow value to move 
seamlessly across borders. Interoperability of blockchains would be essential 
in this process. As such, CryptoSensei highlighted the role of companies like 
Chainlink, Ripple, and others that are connecting real-world assets to 
blockchain platforms, and he specifically called out XRP as having the 
potential to rise to the top. “Obviously, we would love to see XRP number one 
in front of Bitcoin,” he said, adding that the combination of regulation, 
interoperability, and tokenization could make this outcome possible. The 
Altcoin To Surpass Bitcoin? Ripple and its cryptocurrency XRP have long been 
recognized for their strong ties with banks, payment providers, and financial 
institutions worldwide. According to Ripple, XRP was designed with a focus on 
real-world utility in cross-border payments and settlement. Related Reading: 
XRP Price Holds Macro Consolidation Zone, Wave 3 Surge Could Send Price To $5 
This institutional integration distinguishes XRP from Bitcoin, and many 
analysts have argued adoption by financial institutions is the only way XRP 
can beat Bitcoin to become the number one cryptocurrency. Crypto analyst 
BarriC suggested that the adoption of XRP by institutions could see its price 
settle well above $1,000. Another important factor that might cause XRP to 
overtake Bitcoin lies in the growing use of the XRP Ledger for tokenization. 
Real-World Asset (RWA) tokenization has grown massively in the past few weeks 
on the XRP Ledger, with the network growing as the platform for creating and 
managing tokenized assets. Tokenization of real-world assets is viewed by many 
as one of the largest growth opportunities for blockchain technology, with 
trillions of dollars in value expected to migrate on-chain in the next few 
decades. This will undoubtedly bode well for the XRP price if the XRP Ledger 
can capitalize well on the tokenization trend. Featured image from iStock, 
chart from Tradingview.com

XRP To Surpass Bitcoin? Pundit Reveals What Will Drive The Takeover

This content is provided by a sponsor. Trump Media &amp; Technology Group Corp., 
Crypto.com and Yorkville Acquisition Corp. have entered into a definitive 
agreement for a Business Combination to establish Trump Media Group CRO 
Strategy, Inc., a digital asset treasury company focused on the acquisition of 
the native cryptocurrency token of the Cronos ecosystem, CRO. […]

Cryptocurrency price in PHP