I still remember asking my CS teacher about my code:  “Why doesn’t it work?”

“Well, young lady, the program does only what you tell it to do!”

She meant I’d made a mistake, and the machine simply followed my instructions. Ever since then I was frustrated that bugs are always on me and never on the computer. When something doesn’t work, you should ask yourself why - and not the machine.

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Now, when I vibe-code with AI, it’s not just me. It’s me plus a very fast, very confident assistant that happily comes up with anything and everything the prompt didn’t specify. That means my responsibility shifts: I have to set guardrails, give architecture, and verify relentlessly.

When we’re building on the blockchain, especially in DeFi, bugs and mistakes aren’t just a point of frustration: they’re exploits. That’s why blockchain vibe coding demands better-equipped developers who know how to steer an LLM’s reasoning and set their own human constraints to avoid financial risks.

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Why CS fundamentals matter even more with AI

One developer recently shared an example that shows how approaching vibe coding with CS knowledge can improve LLM’s output (if you’re curious, you can find the link at the end of this article). The dev fine-tuned an AI model on months’ worth of Solidity code - the good, the bad, the well-audited, and even the exploited, annotating the data and providing clear instructions on what works and what doesn’t.

Of course, the LLM didn’t become a Solidity prodigy, but hallucinations dropped, and the code didn’t just look neat - it covered most major security cases.

This experiment shows that when you pair an LLM with precise guidance, structured examples, and real engineering understanding (gas quirks, re-entrancy patterns, known exploits) you don’t just vibe code. You turn into the sort of coder an LLM would dream of (if LLMs could dream). So, go ahead and read up on the core CS fundamentals, starting with Git basics and system architecture. That foundation alone will already put you miles ahead.

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DeFi vibe coding mix: MCP, Agents & A2A, x402

Most vibe coding on blockchain ends up touching DeFi. Dashboards, swap UIs, vault tooling, staking flows, liquid restaking trackers, MEV-aware agents - the moment tokens and state enter the picture, you're no longer prototyping UI. You're coordinating money movement. That’s where the toolchain around the LLM matters just as much as the prompt and the training datasets.

At the moment of writing, there are four main frameworks that can meaningfully improve how LLMs perform in DeFi, if used wisely: MCP, AI Agents, A2A, and x402.

1) Model Context Protocol (MCP)

MCP is a protocol that lets an AI assistant access real development context, i.e. your SDKs, RPC clients, program interfaces, docs, and codebase, instead of hallucinating them.

This means that you can connect your LLM to tools and project state explicitly through MCP, so it generates code against your actual tech stack rather than guessing from memory. It’s the equivalent of onboarding a junior engineer by handing them the exact repo and docs instead of letting them guess what transfer() means based on their previous experience.

In the context of creating a DeFi app on Solana, for example, connecting to Solana MCP server will help you enforce correct Solana transaction methods, build an AMM using the official code examples, or even use best practices for handling decimal values in Solana programs. Be mindful of security risks, though, even when using tools like MCP that reduce hallucinations to a minimum (more about security risks below).

As for the common practices for vibe coding Solana, now developers don’t just use Solana MCP, aided by the official Solana program examples, code samples from the Solana cookbook, or their templates as a starting point. Very often, they build AI agents and delegate some tasks to them.

2) AI Agents

AI agents are autonomous software entities powered by LLMs that can observe, reason, and act on the blockchain via defined tools and workflows. They can execute transactions, monitor state, and automate multi-step processes.

These agents are typically built using specialized frameworks and toolkits. For example, the Solana Agent Kit provides an open-source toolkit that connects AI agents to Solana protocols, enabling actions such as token swaps, lending, airdrops, vault interactions, and more.

Solana’s official developer guide also lists agent frameworks like Eliza OS, Rig, and GOAT as available options for building on-chain agents.

When building DeFi apps, you often need more than static UIs or contracts - you need live workflows: monitoring pools, rebalancing positions, triggering hedges, and reacting to on-chain events. AI agents make that possible by becoming the “operator” of those flows. On Solana, where transactions are fast and cheap, agents can execute high-frequency, conditional tasks that would be infeasible manually. For example, an agent built with Solana Agent Kit could monitor multiple AMMs, simulate arbitrage opportunities, and execute trades autonomously.

3) Agent-to-Agent protocol (A2A)

Recently, the conversation has broadened with the rise of A2A systems.

The A2A protocol is a standard that enables AI agents to communicate, collaborate, and delegate tasks across different vendors, frameworks, and platforms.

Some assume A2A replaces MCP, but in fact they solve different problems. MCP anchors the model to the correct tools and data, whereas A2A coordinates multiple agents so they can divide tasks, pass state, and collaborate.

In other words, MCP is like picking up the shovel when you’re asked to dig; A2A is the conversation you have with a co-worker who needs the shovel next.

In practice, many systems use them together: MCP handles context and execution, while A2A manages multi-agent workflows, delegation, and collaboration.

In an agent ecosystem, one agent might handle data ingestion, another agent might compute strategy, and a third agent executes a transaction. A2A facilitates that workflow by providing standardized interfaces for discovery, messaging, and task hand-off. For example, each agent can publish an “Agent Card” describing its capabilities, so other agents know who to call for what.

DeFi often involves complex on-chain workflows: monitoring pool health → simulating swap routes → executing trades → rebalancing positions. Under A2A, these steps can each be handled by separate agents that talk to each other.

• One agent watches oracle feeds and triggers when imbalance arises.
• A second agent simulates the optimal route given token liquidity.
• A third agent signs the transaction and sends it.

Because each agent uses A2A, the orchestration is standardized and interoperable, reducing custom glue code and potential failure points.

4) x402 (Programmable Payments Protocol)

As agents begin executing blockchain-native tasks, the need for MCP and A2A protocols grows, especially with protocols like x402 emerging.

The x402 protocol is a standard (led by Coinbase) enabling instant stablecoin payments directly over HTTP. It turns the long-unused HTTP “402 Payment Required” status code into a functional micro-payment layer.

It lets agents pay each other and APIs programmatically, for example:

• $0.01 to query data
• $0.003 for 100 inference tokens
• $0.15 for GPU minutes
• $0.50 for a one-off document pull
• $0.001 for a liquidity route probe

In a DeFi context, this stack unlocks powerful capabilities: AI agents fetching liquidity data or oracle feeds on demand and paying as they go; automated vault managers compensating data providers in real time; cross-agent workflows where tasks and fees settle programmatically.

Together with the previously described frameworks - MCP (context), Agents (actions), and A2A (coordination) - these pieces form the emerging standard stack for DeFi vibe-coding: models grounded in real tooling, agents orchestrated across scopes, and transactions where value is built into every step.

These frameworks definitely turn simple vibe coding into what would be fair to call AI-assisted development. However, we still haven’t discussed the security risks that must be addressed in any AI project.

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What would a security auditor say?

Ask any smart contract auditor today and you’ll hear the same story: LLM-generated code looks polished - clean structure, consistent naming, thorough comments - yet can conceal dangerous trust breaks.

In 2023, researchers from CertiK and OpenZeppelin publicly demonstrated that ChatGPT-generated Solidity contracts contained exploitable flaws, including missing access control and reentrancy protections. Security researchers were able to drain funds in the test environment by abusing unprotected functions and storage variables.

These weren’t obscure edge cases, they were fundamental security primitives missing because the model “thought” the code looked right. That’s why auditors repeat the same warning: clean & pretty doesn’t equal safe.

For example, here’s just one post by an Oak Security auditor:

LLMs help you ship fast.They also help you ship… bugs.

Across the security community, the guidance is consistent: use LLMs to explore and prototype, not to ship unaudited smart contracts into production.

Common AI-generated smart contract mistakes

Based on findings from a recent paper on vulnerabilities in Solana programs and an industry analysis of smart contract vulnerabilities, here are some of the most frequent security failures observed when AI-generated code is used without rigorous human review and auditing:

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These errors show once again that AI doesn’t struggle with syntax - it struggles with assumptions, context, and security intent. When you vibe-code smart contracts, the danger isn’t that the code won’t compile - it’s that it will compile, look clean, pass a happy-path test, and still be exploitable.

In other words, AI doesn’t let you do less work in DeFi - it shifts the work. You review deeper, model threats earlier, and treat every AI-produced line as hostile until it earns your trust.

So are we right back at square one, with the bugs, errors, and exploits on us again?

Sad but true. Or from Sheldon’s perspective:

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A practical flow: weave your notes into the pipeline

1. Study the landscape first
2. Research existing DeFi architectures, security patterns, and ecosystem-standard frameworks.
3. (If your model learns from real examples, you should too.)
4. Start from proven scaffolds
5. Use pre-built templates, protocol examples, and annotated code, and provide the same to your LLM.
6. Good defaults reduce hallucinations; structure reduces attack surface.
7. Add real context, not vibesConnect your environment via MCP or similar tool layers so the model uses:
   • actual SDKs & program clients
   • real wallet/signing flows
   • correct account models & serialization
   AI works better when it’s coding inside a real repo, not its imagination.
8. Use agents where workflows matter
9. For routine tasks (data pulls, routing checks, dev helpers), let agents orchestrate the flow, not invent logic.
10. When the prototype runs, stop and verifyBefore anything touches real capital:
    • Manual review by experienced smart-contract engineers
      • auth & signer checks (Solana)
      • CEI ordering, reentrancy & permissioning (EVM)
    • Security testing
      • Static + symbolic + fuzz testing
        • EVM: Slither / Mythril / Echidna
        • Solana: account invariant tests, signer/path fuzzing, CPI boundary tests
    • Formal audit
    Hand it to professional security engineers.If it moves money, it needs an audit.
11. Then, and only then, deploy carefully
12. Start with caps, timelocks, upgrade controls & kill switches. Ship like capital depends on it - because it does. Once again: you shouldn’t vibe code in production.

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Further reading & watching

• x402: how it works: https://docs.cdp.coinbase.com/x402/core-concepts/how-it-works
• Solana AI projects: https://x.com/knimkar/status/1863719025500623344
• “I Taught My Smart Contract to Dream” on dev.to: https://dev.to/dhis_is_jj/i-taught-my-smart-contract-to-dream-ai-generated-solidity-code-2ijh
• Using MCP in Claude: https://www.youtube.com/watch?v=8RoNGPFtOhM
• How to build a Solana MCP server with QuickNode: https://www.quicknode.com/guides/ai/solana-mcp-server
• Solana developer templates: https://templates.solana.com/
• Solana AI toolkit: https://solana.com/solutions/ai

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