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Summary

This note tracks the current official product signals around deep research as a category, not just as a single case study. It is meant to help contributors extend the lab’s deep-research coverage with current product positioning from OpenAI and Google.

Why It Matters

Deep research is turning into a recognizable agent shape: long-running, source-aware, analyst-style work that gathers evidence, synthesizes it into a report, and increasingly connects to trusted or private systems. That makes it useful across case studies, platform comparisons, and future starter projects.

Scope Notes

Included:
  • OpenAI’s current deep research positioning in ChatGPT
  • Google’s developer API framing for Gemini Deep Research
  • Google’s June 2026 Interactions API general-availability shift
  • current Google ADK and A2A bridge signals for Interactions-based agents
Excluded:
  • implementation walkthroughs for one specific UI
  • benchmark deep dives beyond what the official product pages mention
  • broader research-assistant tooling that is not framed as deep research

Source Map

Synthesis

Four stable signals stand out across these sources:
  • Deep research is being framed as work product, not just chat. OpenAI describes analyst-level reports built from many sources, while Google frames Deep Research as a long-running synthesis agent.
  • The product boundary is moving beyond public web search. OpenAI added MCP and app connections plus trusted-site restrictions in the February 10, 2026 update. Google keeps pushing the same direction through connected data, managed tools, and long-running interaction state.
  • Google is no longer only saying “Gemini Deep Research exists.” The stronger June 2026 signal is that Interactions API is now the primary interface for Gemini models and agents, with Deep Research becoming one concrete surface on top of that wider runtime model.
  • Developer-facing agent systems are absorbing more of the product shape. Server-side state, resumable background work, tool execution, and A2A bridges now show up in the same interface family that previously looked like a single research-agent feature.
This means the lab should treat deep research as a broader product category with shared traits such as planning, evidence gathering, traceability, artifact quality, and long-running interaction state rather than as one isolated feature from one vendor.

Contributor Implications

Gaps And Follow-up

  • Expand Deep Research Agents with a short product-signals section on Gemini Interactions API as a default developer surface for long-running research agents.
  • Add a follow-up note focused on evaluation and trust boundaries for long-running research agents that mix built-in agents with framework-owned outer loops.
  • Revisit whether a starter project should support trusted-source constraints or server-side interaction state alongside MCP-connected private data in a future iteration.

Update Log

  • 2026-06-24: Refreshed the note around Google’s Interactions API general availability and its new role as a primary Gemini agent interface.
  • 2026-04-23: Added a contributor-facing product-signals note based on current OpenAI and Google sources.