Proof pending. Core topic summary fields are still materializing.
Agentic AI is advancing the capabilities of artificial intelligence systems by integrating real-time feedback mechanisms, fine-grained access controls, and efficient model selection strategies. Current research focuses on enhancing the reliability and efficiency of agentic systems through methods like proactive evaluation during execution, optimizing smaller models for routine tasks, and implementing sanity checks to ensure trustworthy data science outputs. These developments are crucial for builders as they navigate the complexities of deploying AI in various domains, ensuring that systems can operate effectively while adhering to regulatory and operational constraints. The ongoing exploration of agentic AI frameworks aims to balance autonomy and agency, providing a structured approach to design and deployment that prioritizes oversight and accountability.
Topic-specific paper and score movement from the daily diff ledger.
Tool-calling agents are evaluated on tool selection, parameter accuracy, and scope recognition, yet LLM trajectory assessments remain inherently post-hoc. Disconnected from the active execution loop, ...
Recent studies reveal gaps in delegating critical tasks to agentic AI that accesses websites on the user's behalf, primarily due to limited access control mechanisms on websites designed for agentic A...
Agentic data science (ADS) pipelines have grown rapidly in both capability and adoption, with systems such as OpenAI Codex now able to directly analyze datasets and produce answers to statistical ques...
Large language models in Agentic AI systems consume tool schemas and execution results and emit tool invocations as structured data. The default language for that exchange, JSON, was designed for appl...
Production agentic systems make many model calls per user request, and most of those calls are short, structured, and routine. This raises a practical routing question that existing evaluations do not...
Agentic AI failures need post-hoc reconstruction: what the agent did, on whose authority, against which policy, and from what reasoning. Cross-regime feasibility remains unmeasured under one property-...
Agentic AI systems integrating large language models (LLMs) with reasoning and tooluse capabilities are transforming various domains - in particular, software development. In contrast, their applicati...
Agentic artificial intelligence (AI) in organizations is a sequential decision problem constrained by reliability and oversight cost. When deterministic workflows are replaced by stochastic policies o...
Agentic AI is increasingly judged not by fluent output alone but by whether it can act, remember, and verify under partial observability, delay, and strategic observation. Existing research often stud...
Reflexion-style agents rely on self-generated reflections as memory, implicitly assuming that agents can accurately diagnose their own failures.We show that this assumption can fail systematically: ac...
Freshness
Canonical route: /topics
Agent Handoff
Canonical ID agentic-ai | Route /topic/agentic-ai
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/topic/agentic-aiMCP example
{
"tool": "search_papers",
"arguments": {
"query": "Agentic AI",
"cluster": "Agentic AI"
}
}source_context
{
"surface": "topic",
"mode": "topic",
"query": "Agentic AI",
"normalized_query": "agentic-ai",
"route": "/topic/agentic-ai",
"paper_ref": null,
"topic_slug": "agentic-ai",
"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.