arXiv:2606.14155v1 Announce Type: new Abstract: Context adaptation automates prompt engineering in LLM-based systems by iteratively revising tunable prompts from task feedback, without modifying model weights. Extending this paradigm to multi-LLM agentic systems is crucial: existing methods suffer from inaccurate credit assignment and lack convergence guarantees. We propose \textbf{G}raph-based \textbf{T}arget \textbf{B}ack-\textbf{P}ropagation (GTBP), a context adaptation framework for agentic workflows modeled as directed acyclic graphs. GTBP propagates local target outputs backward through
The proliferation of multi-LLM agentic systems necessitates more sophisticated and automated prompt engineering techniques to ensure effective and reliable operation.
This research addresses a fundamental challenge in scaling and coordinating AI agents, improving their context adaptation and overall performance, which is crucial for their commercial viability.
The ability to reliably adapt context in complex multi-LLM systems advances their practical deployment, reducing the need for manual prompt engineering and improving system stability.
- · AI agent developers
- · Enterprises deploying AI agents
- · SaaS companies integrating LLM workflows
- · Researchers in AI agents
- · Manual prompt engineering services
Improved performance and reliability of multi-LLM agentic systems become more common.
Accelerated adoption of AI agents across various industries due to enhanced predictability and autonomy.
The development of even more complex and interconnected AI agent networks, leading to new forms of automated decision-making and workflow orchestration.
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Read at arXiv cs.LG