This January 2026 survey from a 29-author team spanning UIUC, Stanford, Yale, and Microsoft Research offers the most comprehensive taxonomy of agentic reasoning to date. The authors organize the field along three nested layers: foundational single-agent capabilities (planning, tool use, search), self-evolving agents that refine behavior through feedback and memory, and collective multi-agent systems with coordination and shared goals. Crucially, they draw a clean line between in-context reasoning — scaling test-time interaction through structured orchestration — and post-training reasoning, which bakes behavior in via RL and SFT.
The survey arrives at an inflection point. The field has been producing agentic frameworks faster than it can evaluate them, and the lack of a unified conceptual vocabulary has made it difficult to compare approaches or identify genuine progress. By building a three-dimensional roadmap, Wei et al. give researchers and practitioners a shared coordinate system. The distinction between in-context and post-training reasoning is especially valuable — it clarifies why the same underlying model can behave so differently depending on whether intelligence is scaffolded at runtime versus trained in.
For the near term, this survey highlights that the hardest open problems in agentic AI are not about raw capability but about sustained coherence: long-horizon interaction, personalization, and world modeling. The authors flag that most benchmarks still evaluate agents on short, closed tasks — a significant gap given that real deployment involves open-ended, multi-step environments. The governance section is notably substantive, reflecting growing field-wide awareness that autonomous agents operating in high-stakes domains (healthcare, robotics, research) require safety frameworks that don't yet exist.
The survey's coverage of scalable multi-agent training stands out as the most underexplored frontier. While single-agent RL has matured significantly (DeepSeek-R1, o1, etc.), training agents that reliably coordinate at scale remains unsolved. The authors' project page promises an evolving resource as the field moves fast — worth tracking as a living index of what's current in agentic reasoning research.