The Complete Guide to AI Intelligence Performance Optimization
Automated trade optimization based on Solana Agent Kit consists of three layers:
1. Optimization of the base configuration
- Set reasonable operating intervals:
agent.setActionInterval(60000)Avoiding RPC flow limiting - Select premium RPC node: replace default mainnet-beta with a paid node (e.g. QuickNode)
- Enable local transaction caching: via
agent.enableTxCache()Reduce duplicate queries
2. Advanced strategy optimization
- Batch transaction processing: using
tools.batchTransactions()Consolidate multiple transactions - Prioritize the use of compressed airdrops: save 901 TP3Tgas compared to regular transfers
- Integration with the Jupiter API: via
tools.getBestRoute()Getting the Optimal Slip Path
3. Monitoring and tuning
- Deploy Prometheus Monitoring: Tracking Transactions Per Second (TPS), Latency, and Other Metrics
- Analyzing error logs: special attention
TransactionExpiredBlockheightExceededincorrect - Using SIMD instructions: compiling native C++ modules for high-frequency trading scenarios
Typical performance benchmark: optimized for up to 50+ TPS for a single smart body, 3-5x improvement over direct web3.js calls
This answer comes from the articleSolana Agent Kit: an open source toolkit for connecting AI intelligences to the Solana protocolThe
May not be reproduced without permission:AI productivity tools " How to optimize the performance of AI intelligences for automated transactions on the Solana chain?
