agentica-prompts

To fix LLM instruction ambiguity in agent communication, apply structured prompting patterns and system prompt templates that enforce clear agent roles and output formats. This approach mitigates misinterpretations and improves agent task success rates.

The best way to preserve context during multi-agent orchestration is implementing directory-based handoffs. This file-system communication mechanism passes context between agents reliably, preventing data loss during sequential workflow phases like research, planning, and implementation.

To structure prompts for a research, plan, implement, and review workflow, use orchestration patterns with distinct system prompt templates for each phase. Define specific agent roles and expected output formats to ensure seamless handoffs between tasks.

Yes, you can use directory handoffs to pass context between REPL agents. This file-system-based mechanism allows agents to write and read context files sequentially, ensuring context-preserving orchestration across multi-agent systems.

AI agents fail to follow multi-step instructions consistently due to common anti-patterns in agent communication and prompt ambiguity. Implementing structured prompting patterns and clear system prompt templates addresses these issues, improving success rates from 60% to over 95%.

Structured prompt templates are designed for Agentica and REPL agents. They require no external dependencies, providing directory-based handoffs and system prompt templates directly to improve context-preserving orchestration and workflow automation.