Signals via API

Public sample from today's flagship detail endpoint. Public payloads can be limited or redacted. The backend exposes read endpoints for signals, storylines, and briefings.

GET /v1/narratives/e11502ce-9860-48f5-9c46-8aea0c48a335?tenant=ai

{
  "id": "e11502ce-9860-48f5-9c46-8aea0c48a335",
  "title": "El Agente Gr\\'afico: Structured Execution Graphs for Scientific Agents",
  "summary": "arXiv:2602.17902v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to automate scientific workflows, yet their integration with heterogeneous computational tools remains ad hoc and fragile. Current agentic approaches often rely on unstructured text to manage context and coordinate execution, generating often overwhelming volumes of information that may obscure decis",
  "first_seen_at": "2026-02-23T05:00:00Z",
  "last_seen_at": "2026-02-23T17:08:56Z",
  "created_at": "2026-02-23T18:01:15.533150Z",
  "updated_at": "2026-02-23T18:01:17.248957Z",
  "timeframe": "24h",
  "run_id": null,
  "metrics": {
    "divergence": 21.355,
    "post_count": 4,
    "momentum_1h": 1,
    "momentum_7d": 4,
    "score_total": 1.698441,
    "shill_score": 12.5,
    "momentum_24h": 4,
    "market_movers": [],
    "evidence_score": 1,
    "unique_authors": 4,
    "coherence_score": 0.7757,
    "diversity_bonus": 0.25,
    "duplicate_ratio": 0,
    "platforms_count": 2,
    "social_momentum": 4,
    "divergence_label": "chatter_without_pricing",
    "market_conviction": 0,
    "origin_share_top1": 0.25,
    "unique_publishers": 4,
    "origin_layer_posts": 1,
    "source_types_count": 2,
    "unique_authors_24h": 4,
    "unique_origin_urls": 4,
    "amplifier_share_top1": 0.25,
    "author_concentration": 0.25,
    "publisher_share_top1": 0.25,
    "amplifier_layer_posts": 3,
    "concentration_penalty": 0,
    "unique_origin_domains": 4,
    "source_diversity_bonus": 0.2,
    "unique_publisher_types": 3,
    "unique_origin_publishers": 4
  },
  "platforms": [
    "reddit",
    "rss"
  ],
  "top_tickers": [],
  "why_now": [],
  "display_title": "Advancements in Agentic AI and Multi-Agent Coordination",
  "display_summary": "Recent discussions in the AI community emphasize the evolution of agentic AI systems, which are transitioning from basic conversational models to more sophisticated autonomous agents. These systems are designed to handle complex reasoning, tool usage, and decision-making across various applications.",
  "narrative_frame_display": "Recent developments in agentic AI highlight the shift towards multi-agent systems and structured workflows. Researchers are exploring frameworks that enhance the coordination and execution of tasks across various domains, including scientific research and healthcare. The focus is on creating systems that can effectively manage complex interactions and decision-making processes, moving beyond traditional single-model approaches.",
  "display_tags": [
    "ai",
    "aws_machine_learning_blog"
  ],
  "tags": [
    "ai",
    "aws_machine_learning_blog"
  ],
  "cscope_tags": [
    "ai",
    "aws_machine_learning_blog"
  ],
  "why_now_display": [
    "Recent innovations in AI technology are driving the need for more sophisticated agentic systems.",
    "The integration of LLMs with structured frameworks is becoming increasingly relevant as AI applications expand.",
    "Ongoing research and development in this area are critical for addressing the complexities of modern AI tasks."
  ],
  "why_it_matters_display": [
    "The shift to agentic AI represents a significant advancement in AI capabilities, enabling more complex and autonomous systems.",
    "Structured workflows can enhance the reliability and efficiency of AI applications across various sectors, including healthcare and scientific researc",
    "Understanding multi-agent coordination is crucial for developing scalable AI solutions that can adapt to real-world challenges."
  ],
  "show_why": true,
  "sources_display": [
    {
      "url": "https://aws.amazon.com/blogs/machine-learning/agentic-ai-with-multi-model-framework-using-hugging-face-smolagents-on-aws/",
      "label": "aws.amazon.com: AWS Machine Learning Blog",
      "published_at": "2026-02-23T15:47:06+00:00"
    },
    {
      "url": "https://arxiv.org/abs/2602.17902",
      "label": "arxiv.org: arXiv",
      "published_at": "2026-02-23T05:00:00+00:00"
    },
    {
      "url": "https://v.redd.it/uk5lx1do08lg1",
      "label": "v.redd.it: Agentic Workflow Overview + Testing Mistral Models (via Reddit)",
      "published_at": "2026-02-23T10:26:28+00:00"
    },
    {
      "url": "https://www.reddit.com/r/LangChain/comments/1rcn8kj/scaling_intelligence_through_multiagent/",
      "label": "LangChain (via Reddit)",
      "published_at": "2026-02-23T17:08:56+00:00"
    }
  ],
  "llm_status": "ok",
  "llm_meta": {
    "model": "gpt-4o-mini",
    "prompt_version": "enrich_v2",
    "input_hash": "a77ce4d239de420fd700cf119a2c1e735349da7c838221aacee0d4029b4dcc66",
    "updated_at": "2026-02-23T18:04:27.046883+00:00",
    "llm_metadata": {
      "env": "prod",
      "host": "e47ed83e0442",
      "stage": "signals.enrich",
      "run_id": "27f07d0f-42e4-4312-bce7-643f24ad040d",
      "tenant": "ai",
      "service": "api",
      "pipeline": "pipeline_run",
      "correlation_id": "e11502ce-9860-48f5-9c46-8aea0c48a335"
    }
  },
  "story_id": "0f4afc19-b7c6-491b-a7d3-e6ea96e3ae53"
}

• Signals and storylines feed endpoints with filtering and rate limits
• Briefing delivery endpoints for integrations
• Evidence link payloads for auditability

• Route top stories into Slack or Teams for morning and evening desk updates.
• Sync storyline evidence into Notion, Airtable, or internal research trackers.
• Feed metrics into BI dashboards for momentum, concentration, and source mix monitoring.

Quick start endpoints: /v1/feed/stories, /v1/signals, /v1/storylines/search, /v1/briefings/latest.

For product access, see Pricing.