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Sitemap . Product updates DigitalOcean Signals: Observability for AI Agents and Inference By Ravi Verma Updated: October 28, 2026 8 min read --enabled = true The agent ID is the ID of the Environment Config the agent runs from ( doctl harness-runtime config list shows them). Signals also works with Inference Engine conversations. In this preview, enabling and reading Inference signals is available through the API and doctl only, with the Console experience to follow. Use your agents as usual . Signals consumes the same conversation stream that Harness Runtime and Inference Engine produce. There is nothing to instrument and nothing to add to your agent’s code . Signals for a session typically appear within 30 minutes of the session going idle. Read the signals . The Signals view lists recent sessions with their quality rating, signal count, and the concerning flag. Most rows will be unremarkable, because you triage a whole agent at a glance and open only what is flagged. From a session, open its dialogues and jump to the step where a signal fired. From the command line,..."},"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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