A chatbot answers once. An agent loops:
Essential controls:
LLMs are strong general reasoners but weak on private, current, or domain-specific facts. Retrieval-Augmented Generation (RAG) solves this by injecting grounded context at inference time.
A model that looks great in a notebook often fails in production because:
Beyond accuracy:
Base layers optimize decentralization and security, but throughput is limited. Layer 2 systems move execution off-chain while inheriting L1 settlement.
State validity is not enough; users need transaction data to reconstruct state.
DA approaches:
The canonical constant-product AMM uses:
x * y = k
Where x and y are token reserves. A swap changes reserves and implied price. Deeper liquidity means lower slippage.
Lending markets depend on over-collateralization:
Critical parameters:
Top failure modes in DeFi:
Engineering mitigations:
A smart contract is code stored on-chain that executes deterministically on every validating node. The same input must always produce the same state transition.
Core ideas:
Ethereum transactions either transfer ETH or call contract bytecode.
User signs transaction
→ node gossips transaction to mempool
→ validator includes it in a block
→ each node runs EVM bytecode
→ state root updates if execution succeeds
Important mechanics:
Gas prevents denial-of-service. Every opcode has cost. If gas runs out, state changes revert (except gas spent).
Practical design rules:
contract Vault {
mapping(address => uint256) public balances;
function deposit() external payable {
balances[msg.sender] += msg.value;
}
function withdraw(uint256 amount) external {
require(balances[msg.sender] >= amount, "insufficient");
balances[msg.sender] -= amount;
(bool ok, ) = msg.sender.call{value: amount}("");
require(ok, "transfer failed");
}
}
This follows checks-effects-interactions ordering.
Trades -> Enrichment -> Valuation -> Sensitivities -> Aggregation -> Limits -> Dashboard