Proof pending. This topic has not reached the minimum paper threshold yet.
Agentic applications based on large language models increasingly rely on multi-step interaction loops involving planning, action execution, and environment feedback. While such systems are now deploye...
Tool using agents often fail for operational reasons even when language understanding is strong. Common causes include invalid arguments, interface drift, weak recovery, and inefficient retry behavior...
Large language model (LLM) agents perform strongly on short- and mid-horizon tasks, but often break down on long-horizon tasks that require extended, interdependent action sequences. Despite rapid pro...
Large Language Models are increasingly deployed inside agentic systems, where they must follow structured protocols, adapt to evolving states, and operate under memory, latency, and cost constraints. ...
Freshness
Canonical route: /topics
Agent Handoff
Canonical ID agentic-systems | Route /topic/agentic-systems
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/topic/agentic-systemsMCP example
{
"tool": "search_papers",
"arguments": {
"query": "Agentic Systems",
"cluster": "Agentic Systems"
}
}source_context
{
"surface": "topic",
"mode": "topic",
"query": "Agentic Systems",
"normalized_query": "agentic-systems",
"route": "/topic/agentic-systems",
"paper_ref": null,
"topic_slug": "agentic-systems",
"benchmark_ref": null,
"dataset_ref": null
}Use This Via API or MCP
Topic pages bundle paper counts, viability trends, author concentration, and top questions into one canonical surface your agents can reference before they open Signal Canvas or create a workspace.