Avoid a single, confusing chat thread with your agent. Organize conversations by creating separate Telegram groups and sub-topics for different contexts (e.g., 'Content Ideas', 'To-Do Management'). Then, set a unique system prompt for each topic. This ensures the agent always understands the specific context of your conversation.

Instead of running an LLM for recurring tasks, have the Hermes agent write the code once. Combine this with cost-effective models via OpenRouter to dramatically reduce token spend, in one case from $130 to $10 over five days.

Instead of starting new chats for every task, use single, long-running 'monothreads' for each major workstream. Advanced context compaction in tools like Codex allows these threads to persist memory over time, turning the AI from a simple Q&A bot into an ongoing project collaborator with deep context.

The new Codex app encourages a 'monothread' pattern where a single AI conversation is kept alive for weeks. Improved context compaction allows the thread's value to increase over time, moving beyond the old model of starting fresh for each task and creating a persistent, learning assistant.

To manage context costs, Tasklet summarizes agent history with decreasing granularity over time. Recent interactions are sent verbatim, while older conversations have tool calls, thinking steps, and messages truncated or summarized. This is done in cache-aware buckets to minimize cost.

To facilitate high-frequency payments for AI agents, Tempo's Machine Payments Protocol (MPP) introduces Sessions. This feature functions like a bar tab: an agent opens a session (one transaction), makes thousands of API calls off-chain, and then settles the total with a single closing transaction, enabling massive scale.

Long, continuous AI chat threads degrade output quality as the context window fills up, making it harder for the model to recall early details. To maintain high-quality results, treat each discrete feature or task as a new chat, ensuring the agent has a clean, focused context for each job.

Long-running AI agent conversations degrade in quality as the context window fills. The best engineers combat this with "intentional compaction": they direct the agent to summarize its progress into a clean markdown file, then start a fresh session using that summary as the new, clean input. This is like rebooting the agent's short-term memory.

When an AI assistant performs a task like web research, it consumes a large amount of context. Instructing it to use a sub-agent offloads this work, keeping the main chat session lean and focused by only returning the final result, dramatically conserving your context window.