Google opens its deepest research agent to developers: Interactions API + DeepSearchQA, with Search/Finance/Gemini/NotebookLM integrations—timed to OpenAI's GPT-5.2

# Google opens Gemini Deep Research to developers On Dec 11, 2025, Google unveiled a reimagined Gemini Deep Research. It is Google's most advanced research agent so far. For the first time, developers can embed it in third‑party apps. The change ships through a new Interactions API. It lets apps call raw Gemini models or built-in agents. The launch landed the same day OpenAI released GPT‑5.2, raising the stakes for agent platforms. ## Key takeaways - Deep Research is now embeddable in third-party apps through Google's Interactions API. - The API supports server-side state, background execution, and structured tool workflows for long research runs. - Google open-sourced DeepSearchQA to benchmark multi-step, web-scale research behavior. - Integrations are planned for Google Search, Google Finance, the Gemini app, and NotebookLM. - Moroccan startups can turn research into products for tenders, exports, compliance, and multilingual knowledge work. ## What changed in Gemini Deep Research Deep Research is now built on Gemini 3 Pro. Google positions Gemini 3 Pro as its most factual model. Google says it is trained to reduce hallucinations in multi-step work. Before, Deep Research mainly produced long, standalone reports. Now it can plan queries, read sources, revisit them, and fill gaps over time. Google also says it improved performance and cost trade-offs for report-style outputs. Google says Deep Research will also appear in major Google surfaces. The roadmap includes Google Search, Google Finance, the Gemini app, and NotebookLM. That matters for discoverability and daily usage. ## Interactions API: a single endpoint for models and agents Interactions API is the developer entry point. One endpoint can route to a base model like Gemini 3 Pro. The same endpoint can invoke an agent such as `deep-research-pro` preview. The API introduces optional server-side state. That means history can live on Google's side. It reduces client complexity and supports longer sessions. It also supports background execution. Your app can start a job and let it run. That is useful for long inference loops and multi-source reading. Google also exposes an interpretable schema for the agent's steps. It interleaves thoughts, tool calls, and results in a structured way. The goal is debuggable agentic workflows. Finally, the API supports remote MCP tools. In practice, this can let the agent call external tool servers. Think internal search, ticketing, or finance systems, behind your controls. ## DeepSearchQA: open evaluation for deep research Google is also open-sourcing DeepSearchQA. It is a benchmark designed for multi-step research. It targets questions that require broad browsing and synthesis. For developers, benchmarks are more than leaderboard sport. They help you test regressions when you change prompts or tools. They also help you compare agents across vendors on the same tasks. For Moroccan teams, open benchmarks can reduce procurement friction. Many enterprises still ask for evidence of accuracy. A shared test set can ground those discussions in repeatable results. ## Benchmarks, claims, and healthy skepticism Google reports state-of-the-art results for its agent. It cites 46.4% on Humanity's Last Exam (HLE). It also cites 66.1% on DeepSearchQA and 59.2% on Browse/BrowserComp. TechCrunch adds an important nuance. On Google's own tests, the agent leads. On the browser-interaction benchmark, OpenAI ranked slightly higher. Benchmarks still miss real-world constraints. Your domain sources may be paywalled, multilingual, or low quality. Treat the numbers as signals, and test on your own tasks. ## Why the timing matters: distribution meets agents Releasing an embeddable research agent is a platform move. If agents become the primary way users read the web, distribution will matter. Google is pairing capability upgrades with default placement in its products. The same-day GPT‑5.2 release reinforces the pattern. The race is not only model quality. It is also about runtimes, tools, and workflow ownership. For Morocco, this matters because many local builders integrate into global platforms. A stable agent API can lower time-to-market for specialized products. It can also make platform lock-in stronger. ## Morocco's AI context: where a research agent fits Morocco has a growing base of developers, startups, and digital public services. Teams in Casablanca, Rabat, Marrakech, and Tangier often build for SMEs and regulated sectors. Research-heavy work is everywhere, from tenders to compliance. Information is fragmented. Many sources are in French and Arabic, and sometimes Amazigh. Policies and market data often live in PDFs and scattered portals. A deep research agent can reduce manual searching and summarizing. This also matters for public sector modernization. Morocco's Digital Development Agency (ADD) promotes digital transformation. Better research tooling can help agencies draft notes and answer questions with sources. ## Practical product ideas for Moroccan startups The biggest opportunity is vertical research, not generic chat. Build a workflow that ends in a decision, a document, or an action. Use the agent to gather evidence, then keep humans in the approval loop. Ideas that map well to Morocco's economy: - Tender and procurement copilot - Monitor calls for tenders, summarize requirements, and flag missing documents. - Produce a compliance checklist and a draft technical memo with citations. - Export market scout for SMEs - Compare target markets, standards, and competitor positioning. - Generate a source-backed brief for sales teams and distributors. - Banking and fintech research assistant - Track public notices, disclosures, and risk signals from trusted sources. - Draft internal summaries and questions for legal and compliance review. - Tourism and hospitality intelligence - Synthesize advisories, event calendars, and demand signals. - Produce multilingual content briefs grounded in verifiable sources. - Industrial and energy supplier diligence - Compile supplier profiles, certifications, and sustainability statements. - Output a structured vendor dossier for procurement teams. These products get easier when the agent runs in the background. A user can launch a job and return to a finished brief. That matches the Interactions API design. ## Government and public sector uses to watch Research agents can help governments, but governance matters. A good pattern is assist, not decide. The agent drafts, and civil servants verify. High-impact use cases include: - Policy memos that cite official sources and prior decisions. - Comparative research across peer countries for new regulations. - Summaries of stakeholder feedback during consultations. - Internal knowledge bases that point to original documents. Morocco also has clear privacy expectations. The CNDP oversees personal data protection under Law 09-08. Any deployment should limit personal data sharing and log access carefully. ## A Morocco-first checklist: data, language, and trust Research agents fail in predictable ways. Plan for those failures early. It is easier than patching trust later. Checklist for teams shipping in Morocco: - Source strategy - Prefer official portals, primary documents, and named institutions. - Use allowlists for high-stakes workflows, like finance and public policy. - Multilingual handling - Test Arabic and French queries, including mixed-language inputs. - Validate that citations still match after translation or summarization. - Privacy and retention - Redact identifiers before sending content to external models. - Define how long you store prompts, outputs, and tool logs. - Human review gates - Require approval for legal, medical, and financial advice. - Show citations inline, and make the original source one click away. - Cost and latency control - Cap tool calls and re-reads per job. - Cache intermediate results for recurring research tasks. ## How Moroccan developers can start, without overbuilding Google says public beta access is available via Google AI Studio. Start with small, measurable jobs. Avoid building a full agent framework before you have users. A pragmatic path: 1. Pick one research workflow with clear inputs and outputs. 2. Decide if you need a model call or the deep research agent. 3. Define tools the agent can use, including internal search where needed. 4. Use server-side state and background execution for long runs. 5. Create a mini evaluation set, inspired by DeepSearchQA, and rerun it weekly. 6. Ship with citations, logs, and an escalation path to a human. ## Bottom line Google is turning Deep Research from a feature into a platform. Interactions API makes long-running, tool-using research agents easier to embed. DeepSearchQA adds a public yardstick for quality. For Morocco, the near-term value is practical. Use the agent to speed up research in SMEs, public services, and regulated industries. Win on workflow design, language support, and trust, not just model choice.