Achei uma IA

Por favor, habilite o JavaScript para usar esta aplicação.

+d[1].toFixed(3)}); anim4=function(ts){if(!t0)t0=ts;var f=Math.min(1,(ts-t0)/900),e=1-Math.pow(1-f,3); data.forEach(function(d){var p=pts[d[0]],s=start[d[0]];p.x=s.x+(p.tx-s.x)*e;p.y=s.y+(p.ty-s.y)*e;p.g.setAttribute('transform','translate('+p.x+','+p.y+')'); var o=OFF[cur][d[0]];p.t.setAttribute('x',o[0]);p.t.setAttribute('y',o[1]);p.t.setAttribute('text-anchor',o[2])}); if(f>=1)anim4=null}} build4();chips('c4','b',function(b){cur=b;drawScatter(false)});drawScatter(true); function loop(ts){if(active==='p1'&&run1)run1.tick(ts);if(active==='p3')tick3(ts);if(anim4)anim4(ts); if(pts.TinyFish)pts.TinyFish.ring.setAttribute('r',15+3*Math.sin(ts/220));requestAnimationFrame(loop)} requestAnimationFrame(loop); window.addEventListener('load',post);window.addEventListener('resize',post);setTimeout(post,300); })(); </script> </body></html> \"> window.addEventListener(\"message\",function(e){if(e.data&&e.data.mtpTfasH){var f=document.getElementById(\"mtp-tfas-frame\");if(f)f.style.height=e.data.mtpTfasH+\"px\";}}); What did TinyFish learn building deep research? The deep research agent searches, browses, and generates comprehensive, cited reports. Most engineering challenges centered around context management, system orchestration, and product design. Lesson #1: Removing the separate planner Initial iterations used a standalone planner called fresh each time. Only structured state crossed into it. On follow-up queries like “focus only on pricing risks in Southeast Asia,” the agent drifted back to broad market research. TinyFish subsequently moved planning into the orchestrator’s own unified reasoning loop. Lesson #2: Removing subagents: TinyFish initially assigned a separate subagent to each search query, increasing context-switching overhead, latency, and cost. The team later replaced these subagents with tool calls for repeatable tasks such as ranking search results, classifying sources, and extracting evidence. This reduced the information passed back to the orchestrator, allowing it to focus on research planning and deciding what to investigate next. Lesson #3- Matching researchers: TinyFish initially optimized its research system for source coverage, but users placed greater importance on the quality and relevance of supporting evidence. The team adjusted source ranking to account for the research objective and added controls for prioritizing, excluding, or restricting domains. It also developed an internal evaluation harness using an LLM judge to assess reports for relevance, evidence strength, citation trust, and source diversity. How do cost and latency add up? TinyFish budgets the full task, not single calls: Task latency = planning + search + retrieval + evaluation + retries + interactive steps + queueing. Task cost = tokens + tool calls + page or browser usage + retries + infrastructure. SearchAgent-X (ICLR 2026) shows why. Exact retrieval added overhead, and coarse retrieval forced extra reasoning. Its fixes reached up to 3.4x throughput and 5x lower latency versus tested baselines. How does TinyFish compare on search and fetch? OpenBenchmarks fixes the model (gpt-5.6-sol), 100 tasks and budgets, and varies only the provider. Scores are means of 3 runs. Metric TinyFish Exa deep Perplexity Firecrawl Search + fetch completion 79.0% ± 2.0 83.0% ± 1.0 77.7% ± 1.5 76.0% ± 1.0 Median task time 24s 37s 22s 34s Median task cost $0.050 $0.127 $0.083 $0.082 Median task tokens 12,844 23,660 20,062 17,379 Search-only completion 59.3% 66.3% (Exa fast) 77.3% 70.3% Search list price $0 $0.012 $0.005 $0.005 Source: OpenBenchmarks board , updated 26 Sept 2026. Tasks cover enterprise documentation only. OpenBenchmarks also reports search-only results separately. In that configuration, TinyFish achieved 59.3% task completion, compared with 77.3% for Perplexity, 70.3% for Firecrawl, and 66.3% for Exa Fast. These results should not be directly compared with the search-and-fetch scores because the available retrieval capabilities differ. Key Takeaways Control the whole trajectory, not 1 search call. Route by information gap; escalate only when access fails. Keep planning and execution in 1 reasoning loop. Move deterministic steps into tool calls, not subagents. Rank sources by research objective, not one quality score. Read the architecture guide , the deep research lessons and the TinyFish docs . Thanks to the Tinyfish team for the thought leadership / resources for this article. This article is sponsored by TinyFish. The post TinyFish Breaks Down Agentic Search Architecture for Production AI Agents appeared first on MarkTechPost .","datePublished":"2026-10-23T08:58:47+00:00","dateModified":"2026-10-24T08:02:08.007358+00:00","image":"https://s.w.org/images/core/emoji/17.0.2/72x72/2196.png","publisher":{"@type":"Organization","name":"Achei uma IA","url":"https://acheiumaia.com.br"}}