Agents concept guide
AI Agents
AI systems that plan, call tools, observe results, and iterate toward a goal.
Linked research
12
Published MachinoAI explainers currently connected to this concept.
Questions and answers
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Research papers
Papers that connect to AI Agents
OpenForgeRL
OpenForgeRL connects real agent harnesses, remote environments, and standard RL infrastructure so agents can be trained in deployment-like conditions.
Gemini 4 Argon
Gemini 4 Argon targets long-horizon, multimodal professional workflows with a 1M-token output ceiling and a staged safety-first rollout.
Agent-Editing World Model
AEWM turns world modeling into agent-state judgment and revision: keep useful decisions, edit noisy ones, then ground the trajectory with real execution.
Jev AI
Jev is TypeSafe AI’s first System One Model: state in, typed probabilistic decisions out.
GPT-6 Sol
A source-grounded deep dive into GPT-6 Sol covering model behavior, API/runtime design, benchmarks, effort economics, safety, limitations, and production agent architecture.
Grok 4.7 — Long-Horizon Agents
A source-grounded technical walkthrough of Grok 4.7 training changes, runtime controls, multimodal input, tool use, context management and long-horizon agent engineering.
Grok 4.7 — Production
A systems-oriented deployment guide covering the Grok 4.7 API, agent loop, caching, long-context economics, rate limits, observability and safety boundaries.
Opus 5.5 — Production Economics
Production economics view: compare unit price, task trajectory, code volume, review effort, and verification cost.
Opus 5.5 — What Changed
A plain-English model overview focused on what changed from Opus 5 and what those changes mean in daily use.
AgentActionBench
A process-level benchmark for reliable research-agent execution.
Magenta
Verification-guided mathematical reasoning with Lean 4.
ScientistTwo
Autonomous ML research with explicit experiment, ablation and review loops.