{"schema_version":"onlylabs.public_signal.v1","title":"DigitalOcean (GradientAI) Writing: Introducing DigitalOcean Signals and Simulations: see how your agents and models really behave in production, then test the fix before it ships","description":"DigitalOcean (GradientAI) writing signal with public source context, captured evidence pages, related signals, and category-scoped analysis context.","url":"https://onlylabs.fyi/signals/0b938923-b97a-4683-b8a0-8fe1ebeb5762","json_url":"https://onlylabs.fyi/signals/0b938923-b97a-4683-b8a0-8fe1ebeb5762/signal.json","generated_at":"2026-10-12T19:44:59.315Z","evidence_latest_fetched_at":"2026-10-12T16:02:58.496+00:00","signal_first_seen_at":"2026-10-12T16:00:49.317135+00:00","org":{"slug":"digitalocean","name":"DigitalOcean (GradientAI)","category":"neocloud","category_label":"Neocloud","dossier_url":"https://onlylabs.fyi/labs/digitalocean","dossier_json_url":"https://onlylabs.fyi/labs/digitalocean/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/0b938923-b97a-4683-b8a0-8fe1ebeb5762","signal_json":"https://onlylabs.fyi/signals/0b938923-b97a-4683-b8a0-8fe1ebeb5762/signal.json","source":"https://www.digitalocean.com/blog/introducing-signals-and-simulations","lab_dossier":"https://onlylabs.fyi/labs/digitalocean","lab_dossier_json":"https://onlylabs.fyi/labs/digitalocean/dossier.json","analysis":"https://onlylabs.fyi/analysis/digitalocean","analysis_json":"https://onlylabs.fyi/analysis/digitalocean/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/digitalocean/evidence.json","category":"https://onlylabs.fyi/neoclouds","category_json":"https://onlylabs.fyi/neoclouds.json","category_feed":"https://onlylabs.fyi/neoclouds/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json?category=neocloud","topic":"https://onlylabs.fyi/topics/talking","topic_signals_json":"https://onlylabs.fyi/topics/talking/signals.json?category=neocloud","topic_feed":"https://onlylabs.fyi/topics/talking/feed.xml?category=neocloud","data_business":null},"answer_pack":{"answer":"DigitalOcean (GradientAI) published Introducing DigitalOcean Signals and Simulations: see how your agents and models really behave in production, then test the fix before it ships. 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This talking signal gives public context for research themes, product direction, policy, or launch framing. High-signal details: Product launch for AI observability · Signals: Trajectory Sampling and Triage for Agentic Interactions arXiv is now an independent nonprofit! 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Sitemap . Product updates Introducing DigitalOcean Signals and Simulations: see how your agents and models really behave in production, then test the fix before it ships By DigitalOcean Updated: October 11, 2026 5 min read <- Back to blog home You ship an agent. It mostly works. Then the feedback loop becomes a support ticket: a user got the wrong answer, a tool call looped four times, a refund went out that shouldn’t have. Standard observability tells you the system was up and the latency was fine. It can’t tell you the agent chose the wrong tool, used stale context, or made a frustrated user give up. And when you do find the problem, writing realistic test cases to validate a fix means hours of digging through traces or inventing scenarios you hope resemble reality. Today we’re launching two capabilities, part of DigitalOcean Agent Learning Services, that address both halves of that problem. Signals watches production traffic on DigitalOcean Managed Agents and DigitalOcean Inference Engine , and..."},"evidence_pages":[{"is_primary":false,"source_match":false,"url":"https://arxiv.org/pdf/2604.00356","final_url":"https://arxiv.org/pdf/2604.00356","title":"Introducing DigitalOcean Signals and Simulations: see how your agents and models really behave in production, then test the fix before it ships","http_status":200,"content_type":"application/pdf","capture_method":"exa","fetched_at":"2026-10-12T16:02:58.496+00:00","bytes":223837,"raw_path":"9bdaab81edc0a11aeb770c412f087bdd2842b77979e53970565561349cada036.pdf","content_hash":"ca8f534c6a5702d540ab7df36e580df923772faab7316e056c80e74f6f04e29b","excerpt_chars":1200,"truncated":true,"excerpt":"Signals: Trajectory Sampling and Triage for Agentic Interactions arXiv is now an independent nonprofit! Learn more× Signals: Trajectory Sampling and Triage for Agentic Interactions Shuguang Chen Adil Hafeez Salman Paracha Affiliation: [0.5ex] DigitalOcean Holdings, Inc. Affiliation: [0.5ex] {schen, ahafeez, salmanparacha}@digitalocean.com Abstract 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 deployed at scale, improving them post-deployment remains challenging. Agent trajectories are voluminous and non-deterministic, and reviewing each one, whether through human review or auxiliary LLMs, is slow and cost-prohibitive. We propose a lightweight, signal-based framework for triaging agentic interaction trajectories. Our approach computes cheap, broadly applicable signals from live interactions and attaches them as structured attributes for trajectory triage, identifying interactions likely to be informative without affecting online agent behavior. 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