[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-c1e89f89899ba1eb-building-production-ready-rag-agents-on-google-clo-summary":3,"summaries-facets-categories":214,"summary-related-c1e89f89899ba1eb-building-production-ready-rag-agents-on-google-clo-summary":7920},{"id":4,"title":5,"ai":6,"body":13,"categories":166,"created_at":168,"date_modified":168,"description":154,"extension":169,"faq":168,"featured":170,"kicker_label":168,"meta":171,"navigation":193,"path":194,"published_at":195,"question":168,"scraped_at":196,"seo":197,"sitemap":198,"source_id":199,"source_name":200,"source_type":201,"source_url":202,"stem":203,"tags":204,"thumbnail_url":209,"tldr":210,"tweet":211,"unknown_tags":212,"__hash__":213},"summaries\u002Fsummaries\u002Fc1e89f89899ba1eb-building-production-ready-rag-agents-on-google-clo-summary.md","Building Production-Ready RAG Agents on Google Cloud",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","google\u002Fgemini-3.1-flash-lite",9865,1049,4854,0.00403975,{"type":14,"value":15,"toc":153},"minimark",[16,21,25,29,32,55,59,64,67,71,99,103,106,117,121],[17,18,20],"h2",{"id":19},"the-challenge-moving-from-prototype-to-production","The Challenge: Moving from Prototype to Production",[22,23,24],"p",{},"Building a local AI prototype is straightforward, but transitioning to a production-ready workload introduces significant \"Day 2\" operational challenges. These include managing conversational session state across instances, securing API credentials, and grounding models in proprietary data to prevent hallucinations. This session demonstrates how to bridge this gap using Google Cloud's serverless architecture.",[17,26,28],{"id":27},"architectural-pillars-of-an-ai-agent","Architectural Pillars of an AI Agent",[22,30,31],{},"An effective AI agent is defined by three primary components:",[33,34,35,43,49],"ol",{},[36,37,38,42],"li",{},[39,40,41],"strong",{},"The Reasoning Engine (Model):"," The core brain (e.g., Gemini Flash) responsible for intent analysis and natural language understanding.",[36,44,45,48],{},[39,46,47],{},"The Tools (Grounding Layer):"," Custom functions that connect the model to real-world data sources, ensuring responses are factual.",[36,50,51,54],{},[39,52,53],{},"The Orchestration (Agent):"," The logic provided by the Agent Development Kit (ADK) that binds system instructions, tools, and session state together.",[17,56,58],{"id":57},"implementation-workflow","Implementation Workflow",[60,61,63],"h3",{"id":62},"_1-grounding-with-rag","1. Grounding with RAG",[22,65,66],{},"To prevent hallucinations, the agent must consult a factual data source before responding. The workshop utilizes a \"coffee shop\" scenario where the agent fetches menu items, prices, and allergen information via a custom Python function. While file-based RAG (JSON) is sufficient for small prototypes, the speakers emphasize that as the data catalog grows, developers should migrate to vector databases (like Firestore) to avoid context stuffing, which increases token costs, latency, and the risk of \"context collapse.\"",[60,68,70],{"id":69},"_2-developing-the-agent-and-ui","2. Developing the Agent and UI",[72,73,74,93],"ul",{},[36,75,76,79,80,84,85,88,89,92],{},[39,77,78],{},"Agent Logic:"," The ",[81,82,83],"code",{},"agent.py"," file uses the ",[81,86,87],{},"LLM"," agent class from the ADK. It defines a persona (e.g., a friendly barista) and codifies rules, such as restricting recommendations to items returned by the ",[81,90,91],{},"get_menu"," function.",[36,94,95,98],{},[39,96,97],{},"Frontend:"," A Streamlit interface provides a chat-based UI. The application is designed to be modular, allowing the backend to switch from static file retrieval to dynamic database queries without changing the UI layer.",[60,100,102],{"id":101},"_3-secure-deployment-via-cloud-run","3. Secure Deployment via Cloud Run",[22,104,105],{},"Security is enforced through the principle of least privilege. Instead of using the default Compute Engine service account, the process involves:",[72,107,108,111,114],{},[36,109,110],{},"Creating a dedicated IAM service account for the application.",[36,112,113],{},"Deploying via source-based deployment using Cloud Buildpacks, which automates containerization.",[36,115,116],{},"Leveraging Cloud Run's ability to scale to zero, provide secure HTTPS endpoints, and support long-lived connections for streaming responses.",[17,118,120],{"id":119},"key-takeaways","Key Takeaways",[72,122,123,129,135,141,147],{},[36,124,125,128],{},[39,126,127],{},"Use Dedicated Service Accounts:"," Never use the default Compute Engine service account for production apps; create a dedicated IAM identity to limit the blast radius of potential security flaws.",[36,130,131,134],{},[39,132,133],{},"Start Simple, Scale Smart:"," Use file-based RAG for initial prototyping, but plan for vector databases (e.g., Firestore) as soon as you move to production to handle larger datasets efficiently.",[36,136,137,140],{},[39,138,139],{},"Avoid Context Stuffing:"," Do not inject entire catalogs into every prompt; use vector search to retrieve only the relevant context to manage token costs and model performance.",[36,142,143,146],{},[39,144,145],{},"Leverage Serverless Runtimes:"," Cloud Run is ideal for agents because it handles scaling, HTTPS, and streaming connections natively.",[36,148,149,152],{},[39,150,151],{},"Modularize Your Agent:"," Keep your reasoning logic (ADK) separate from your UI (Streamlit) and your data layer to ensure the application remains maintainable as it evolves.",{"title":154,"searchDepth":155,"depth":155,"links":156},"",2,[157,158,159,165],{"id":19,"depth":155,"text":20},{"id":27,"depth":155,"text":28},{"id":57,"depth":155,"text":58,"children":160},[161,163,164],{"id":62,"depth":162,"text":63},3,{"id":69,"depth":162,"text":70},{"id":101,"depth":162,"text":102},{"id":119,"depth":155,"text":120},[167],"AI & LLMs",null,"md",false,{"content_references":172,"triage":188},[173,178,181,184,186],{"type":174,"title":175,"url":176,"context":177},"tool","Google Agent Development Kit (ADK)","https:\u002F\u002Fg.dev\u002Fcloud\u002Frag-agent-adk","recommended",{"type":174,"title":179,"url":180,"context":177},"Cloud Run","https:\u002F\u002Fg.dev\u002Fcloud\u002Fcloud-run-console",{"type":174,"title":182,"context":183},"Streamlit","mentioned",{"type":174,"title":185,"context":183},"Gemini Flash",{"type":174,"title":187,"context":183},"Cloud Firestore",{"relevance":189,"novelty":190,"quality":190,"actionability":189,"composite":191,"reasoning":192},5,4,4.55,"Category: AI & LLMs. The article provides a detailed guide on building production-ready RAG agents, addressing specific pain points like transitioning from prototypes to production and grounding models in real data. It includes actionable steps for implementation, such as using vector databases and developing agent logic, making it highly relevant for the target audience.",true,"\u002Fsummaries\u002Fc1e89f89899ba1eb-building-production-ready-rag-agents-on-google-clo-summary","2026-09-01 20:45:38","2026-09-02 03:13:55",{"title":5,"description":154},{"loc":194},"c1e89f89899ba1eb","Google Cloud Tech","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=sWHMIjgcxEU","summaries\u002Fc1e89f89899ba1eb-building-production-ready-rag-agents-on-google-clo-summary",[205,206,207,208],"llm","agents","python","cloud-run","https:\u002F\u002Fi.ytimg.com\u002Fvi\u002FsWHMIjgcxEU\u002Fhqdefault.jpg","Learn to build and deploy a secure, grounded RAG agent using the Google Agent Development Kit (ADK), Streamlit, and Cloud Run, moving from local prototypes to enterprise-ready infrastructure.","This video is a guided walkthrough of a [Google Codelab](https:\u002F\u002Fg.dev\u002Fcloud\u002Frag-agent-adk) that demonstrates how to build and deploy a RAG-enabled AI agent. The presenters use [Streamlit](https:\u002F\u002Fstreamlit.io) for the frontend, the [Google Agent Development Kit](https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fagent-development-kit) for orchestration, and [Cloud Run](https:\u002F\u002Fg.dev\u002Fcloud\u002Fcloud-run-console) for the production deployment.",[208],"ZM0xidsOTnDMAE45N5XQoiM47xf2ir1mZ869meNEKL4",[215,217,220,222,225,227,230,233,235,237,239,241,244,247,249,251,253,255,258,260,262,264,266,269,271,273,275,277,279,281,283,285,287,289,291,293,295,297,299,301,303,305,307,309,311,313,315,317,319,321,324,326,328,330,332,334,336,338,340,342,344,346,348,350,352,354,356,359,361,363,365,367,369,371,373,375,377,379,381,383,385,387,389,391,393,395,397,399,402,404,406,408,410,412,414,416,418,420,422,424,426,428,430,432,434,436,439,441,443,445,447,449,451,453,455,457,459,461,463,465,467,469,471,473,475,477,479,481,483,485,487,489,491,493,495,497,499,501,503,505,507,509,512,514,516,518,520,522,524,526,528,530,532,535,537,539,541,543,545,547,549,551,553,555,557,559,561,563,565,568,570,572,574,576,578,580,582,584,586,588,590,592,594,597,599,601,603,605,607,609,611,613,615,617,619,621,623,625,627,629,631,633,635,637,639,641,643,645,647,649,651,653,655,657,659,661,663,666,668,670,673,675,677,679,681,683,685,687,689,691,693,695,697,699,701,703,705,707,709,711,714,716,718,720,722,724,726,728,730,732,734,736,738,741,743,745,747,749,751,753,755,757,759,761,763,765,767,769,771,773,775,777,779,781,783,785,787,789,791,793,795,797,799,801,803,805,807,809,811,813,815,817,819,821,823,825,827,829,831,833,835,837,839,841,843,845,847,849,851,853,855,857,859,861,863,865,867,869,871,873,875,877,879,881,883,885,887,889,891,893,895,897,899,901,903,905,907,909,911,913,915,917,919,921,923,925,927,929,931,933,935,937,939,941,943,945,947,949,951,953,955,957,959,961,963,965,967,969,971,973,975,977,979,981,983,985,987,989,991,993,995,997,999,1001,1003,1005,1007,1009,1011,1013,1015,1017,1019,1021,1023,1025,1027,1029,1031,1033,1035,1037,1039,1041,1043,1045,1047,1049,1051,1053,1055,1058,1060,1062,1064,1066,1069,1071,1073,1075,1077,1079,1081,1083,1085,1087,1089,1091,1093,1095,1097,1100,1102,1104,1106,1108,1110,1112,1114,1116,1118,1120,1122,1124,1126,1128,1130,1132,1134,1136,1138,1140,1142,1144,1146,1148,1150,1152,1154,1156,1158,1160,1162,1164,1166,1168,1170,1172,1174,1176,1178,1180,1182,1184,1186,1188,1190,1192,1194,1196,1198,1200,1202,1204,1206,1208,1210,1212,1214,1216,1218,1220,1222,1224,1226,1228,1230,1232,1234,1236,1238,1240,1242,1244,1246,1248,1250,1252,1254,1256,1258,1260,1262,1264,1266,1268,1270,1272,1274,1276,1278,1280,1282,1284,1286,1288,1290,1292,1294,1296,1298,1300,1302,1304,1306,1308,1310,1312,1314,1316,1318,1320,1322,1324,1326,1328,1330,1332,1334,1336,1338,1340,1342,1344,1346,1348,1350,1352,1354,1356,1358,1360,1362,1364,1366,1368,1370,1372,1374,1376,1378,1380,1382,1384,1386,1388,1390,1392,1394,1396,1398,1400,1402,1404,1406,1408,1410,1412,1414,1416,1418,1420,1422,1424,1426,1428,1430,1432,1434,1436,1438,1440,1442,1444,1446,1448,1450,1453,1455,1457,1459,1461,1463,1465,1467,1469,1471,1473,1475,1477,1479,1481,1483,1485,1487,1489,1491,1493,1495,1497,1499,1501,1503,1505,1507,1509,1511,1513,1515,1517,1519,1521,1523,1525,1527,1529,1531,1533,1535,1537,1539,1541,1543,1545,1547,1549,1551,1553,1555,1557,1559,1561,1563,1565,1567,1569,1571,1573,1575,1577,1579,1581,1583,1585,1587,1589,1591,1593,1595,1597,1599,1601,1603,1605,1607,1609,1611,1613,1615,1617,1619,1621,1623,1625,1627,1629,1631,1633,1635,1637,1639,1641,1643,1645,1647,1649,1651,1653,1655,1657,1659,1661,1663,1665,1667,1669,1671,1673,1675,1677,1679,1681,1683,1685,1688,1690,1692,1694,1696,1698,1700,1702,1704,1706,1708,1710,1712,1714,1716,1718,1720,1722,1724,1726,1728,1730,1732,1734,1736,1738,1740,1742,1744,1746,1748,1750,1752,1754,1756,1758,1760,1762,1764,1766,1768,1770,1772,1774,1776,1778,1780,1782,1784,1786,1788,1790,1792,1794,1796,1798,1800,1802,1804,1806,1808,1810,1812,1814,1816,1818,1820,1822,1824,1826,1828,1830,1832,1834,1836,1838,1840,1842,1844,1846,1849,1851,1853,1855,1857,1859,1861,1863,1865,1867,1869,1871,1873,1875,1877,1879,1881,1883,1885,1887,1889,1891,1893,1895,1897,1899,1901,1903,1905,1907,1909,1911,1913,1915,1917,1919,1921,1923,1925,1927,1929,1931,1933,1935,1937,1939,1941,1943,1945,1947,1949,1951,1953,1955,1957,1959,1961,1963,1965,1967,1969,1971,1973,1975,1977,1979,1981,1983,1985,1987,1989,1991,1993,1995,1997,1999,2001,2003,2005,2007,2009,2011,2013,2015,2017,2019,2021,2023,2025,2027,2029,2032,2034,2036,2038,2040,2042,2044,2046,2048,2050,2052,2054,2056,2058,2060,2062,2064,2066,2068,2070,2072,2074,2076,2078,2080,2082,2084,2086,2088,2090,2092,2094,2096,2098,2100,2103,2105,2107,2109,2111,2113,2115,2117,2119,2121,2123,2125,2127,2129,2131,2133,2135,2137,2139,2141,2143,2145,2147,2149,2151,2153,2155,2157,2159,2161,2163,2165,2167,2169,2171,2173,2175,2177,2179,2181,2183,2185,2187,2189,2191,2193,2195,2197,2199,2201,2203,2205,2207,2209,2211,2213,2215,2217,2219,2221,2223,2225,2227,2229,2231,2233,2235,2237,2239,2241,2243,2245,2247,2249,2251,2253,2255,2257,2259,2261,2263,2265,2267,2269,2271,2273,2275,2277,2279,2281,2283,2285,2287,2289,2291,2293,2295,2297,2299,2301,2303,2305,2307,2309,2311,2313,2315,2317,2319,2321,2323,2325,2327,2329,2331,2333,2335,2337,2339,2341,2343,2345,2347,2349,2351,2353,2355,2357,2359,2361,2363,2365,2367,23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Coding LLM for Agents with Tool Calling",{"provider":7,"model":7925,"input_tokens":7926,"output_tokens":7927,"processing_time_ms":7928,"cost_usd":7929},"x-ai\u002Fgrok-4.1-fast",5328,1737,10140,0.00191145,{"type":14,"value":7931,"toc":8023},[7932,7936,7955,7958,7962,7993,7997],[17,7933,7935],{"id":7934},"core-features-and-quick-inference","Core Features and Quick Inference",[22,7937,7938,7939,7942,7943,7946,7947,7950,7951,7954],{},"Qwen3-Coder-Next runs in non-thinking mode without generating ",[81,7940,7941],{},"\u003Cthink>\u003C\u002Fthink>"," blocks, simplifying outputs for coding tasks. Load it via ",[81,7944,7945],{},"transformers"," (latest version) with ",[81,7948,7949],{},"torch_dtype=\"auto\""," and ",[81,7952,7953],{},"device_map=\"auto\""," for automatic hardware placement. Use chat template for prompts like \"Write a quick sort algorithm,\" generating up to 65,536 new tokens. To avoid OOM errors, cap context at 32,768 tokens. Local apps like Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers support it out-of-the-box, enabling fast prototyping without cloud dependency.",[22,7956,7957],{},"Benchmarks (via images) show top performance on coding evals like SWE-Bench Verified, positioning it for agentic coding over general models.",[17,7959,7961],{"id":7960},"efficient-deployment-for-production","Efficient Deployment for Production",[22,7963,7964,7965,7968,7969,7972,7973,7976,7977,7983,7984,7987,7988,7992],{},"Serve with OpenAI-compatible APIs using SGLang (>=v0.5.8, ",[81,7966,7967],{},"pip install 'sglang[app]>=v0.5.8'",") or vLLM (>=0.15.0, ",[81,7970,7971],{},"pip install 'vllm>=0.15.0'","). For SGLang: ",[81,7974,7975],{},"python -m sglang.launch_server --model Qwen\u002FQwen3-Coder-Next --port 30000 --tp-size 2 --tool-call-parser qwen3_coder"," starts at ",[7978,7979,7980],"a",{"href":7980,"rel":7981},"http:\u002F\u002Flocalhost:30000\u002Fv1",[7982],"nofollow"," with 256K context on 2 GPUs (tensor parallel). vLLM: ",[81,7985,7986],{},"vllm serve Qwen\u002FQwen3-Coder-Next --port 8000 --tensor-parallel-size 2 --enable-auto-tool-choice --tool-call-parser qwen3_coder"," at ",[7978,7989,7990],{"href":7990,"rel":7991},"http:\u002F\u002Flocalhost:8000\u002Fv1",[7982],". Reduce to 32,768 context if startup fails due to memory limits, trading length for reliability on smaller hardware.",[17,7994,7996],{"id":7995},"agentic-workflows-and-optimization","Agentic Workflows and Optimization",[22,7998,7999,8000,8003,8004,8007,8008,8011,8012,8015,8016,8015,8019,8022],{},"Define JSON tools (e.g., ",[81,8001,8002],{},"square_the_number"," function taking ",[81,8005,8006],{},"input_num: number",") and call via OpenAI client against local endpoint: ",[81,8009,8010],{},"client.chat.completions.create(..., tools=tools)",". Model handles function calling natively without thinking tokens. For best results, sample at ",[81,8013,8014],{},"temperature=1.0",", ",[81,8017,8018],{},"top_p=0.95",[81,8020,8021],{},"top_k=40"," to balance creativity and focus in code generation. Full details in linked blog, GitHub, and docs; cite the Qwen3-Coder-Next tech report for production use.",{"title":154,"searchDepth":155,"depth":155,"links":8024},[8025,8026,8027],{"id":7934,"depth":155,"text":7935},{"id":7960,"depth":155,"text":7961},{"id":7995,"depth":155,"text":7996},[],{"content_references":8030,"triage":8053},[8031,8037,8041,8044,8047,8050],{"type":8032,"title":8033,"author":8034,"url":8035,"context":8036},"report","Qwen3-Coder-Next Technical Report","Qwen Team","https:\u002F\u002Fgithub.com\u002FQwenLM\u002FQwen3-Coder\u002Fblob\u002Fmain\u002Fqwen3_coder_next_tech_report.pdf","cited",{"type":8038,"title":8039,"url":8040,"context":183},"other","Qwen3-Coder-Next blog","https:\u002F\u002Fqwen.ai\u002Fblog?id=qwen3-coder-next",{"type":8038,"title":8042,"url":8043,"context":183},"Qwen3-Coder GitHub","https:\u002F\u002Fgithub.com\u002FQwenLM\u002FQwen3-Coder",{"type":8038,"title":8045,"url":8046,"context":183},"Qwen Documentation","https:\u002F\u002Fqwen.readthedocs.io\u002Fen\u002Flatest\u002F",{"type":174,"title":8048,"url":8049,"context":177},"SGLang","https:\u002F\u002Fgithub.com\u002Fsgl-project\u002Fsglang",{"type":174,"title":8051,"url":8052,"context":177},"vLLM","https:\u002F\u002Fgithub.com\u002Fvllm-project\u002Fvllm",{"relevance":189,"novelty":190,"quality":190,"actionability":189,"composite":191,"reasoning":8054},"Category: AI & LLMs. The article provides in-depth technical details about the Qwen3-Coder-Next model, including its deployment and usage for coding agents, which directly addresses the needs of developers looking to integrate AI into their products. It offers actionable steps for deployment and optimization, making it highly relevant and practical for the target audience.","\u002Fsummaries\u002Fd5c7b26fc3a6353b-qwen3-coder-next-coding-llm-for-agents-with-tool-c-summary","2026-04-15 15:35:14",{"title":7923,"description":154},{"loc":8055},"d5c7b26fc3a6353b","__oneoff__","article","https:\u002F\u002Fhuggingface.co\u002FQwen\u002FQwen3-Coder-Next","summaries\u002Fd5c7b26fc3a6353b-qwen3-coder-next-coding-llm-for-agents-with-tool-c-summary",[205,206,207],"Qwen3-Coder-Next is an open-weight model optimized for coding agents, featuring non-thinking mode, 256K context, strong benchmarks, and easy deployment via transformers, SGLang, or vLLM for local dev and tool use.",[],"-Kmf7Ahy-Hq9bPYNqqWxKfn6saDo7KGeACSG7NKydSg",{"id":8069,"title":8070,"ai":8071,"body":8076,"categories":8154,"created_at":168,"date_modified":168,"description":154,"extension":169,"faq":168,"featured":170,"kicker_label":168,"meta":8155,"navigation":193,"path":8164,"published_at":8165,"question":168,"scraped_at":8165,"seo":8166,"sitemap":8167,"source_id":8168,"source_name":8169,"source_type":8061,"source_url":8170,"stem":8171,"tags":8172,"thumbnail_url":168,"tldr":8174,"tweet":168,"unknown_tags":8175,"__hash__":8176},"summaries\u002Fsummaries\u002F5c76f9e2f8d51cc7-jaz-a-minimalist-agent-framework-using-code-as-a-h-summary.md","JAZ: A Minimalist Agent Framework Using Code as a Harness",{"provider":7,"model":8,"input_tokens":8072,"output_tokens":8073,"processing_time_ms":8074,"cost_usd":8075},6659,584,3182,0.00254075,{"type":14,"value":8077,"toc":8149},[8078,8082,8089,8093,8099,8119,8125,8129,8132,8146],[17,8079,8081],{"id":8080},"the-case-for-minimalist-agent-architectures","The Case for Minimalist Agent Architectures",[22,8083,8084,8085,8088],{},"Modern agent frameworks often rely on complex, specialized systems for memory management, file handling, and self-improvement. The authors argue that these external harnesses are often unnecessary. They introduce JAZ, a framework that reduces agent architecture to a single, recursive primitive called ",[81,8086,8087],{},"invoke",". By treating the agent loop as a language primitive rather than an external wrapper, JAZ achieves high expressivity with minimal overhead.",[17,8090,8092],{"id":8091},"the-invoke-primitive","The 'Invoke' Primitive",[22,8094,8095,8096,8098],{},"JAZ is built on two core principles that define its ",[81,8097,8087],{}," primitive:",[33,8100,8101,8110],{},[36,8102,8103,8106,8107,8109],{},[39,8104,8105],{},"Recursive Execution:"," The LLM is empowered to write arbitrary executable code that can call ",[81,8108,8087],{}," recursively, allowing the model to manage its own control flow and state transitions.",[36,8111,8112,8115,8116,8118],{},[39,8113,8114],{},"Environment Transparency:"," All inputs to ",[81,8117,8087],{}," and the entire interaction history are exposed as variables within the code environment. This allows the LLM to treat its own history and context as manipulatable data.",[22,8120,8121,8122,8124],{},"By viewing ",[81,8123,8087],{}," as a function whose implementation is provided by the LLM at runtime, the framework eliminates the need for manually designed tools or rigid external systems. The system provides basic hooks for monitoring and constraints, but the logic remains entirely within the LLM's generated code.",[17,8126,8128],{"id":8127},"performance-and-efficiency","Performance and Efficiency",[22,8130,8131],{},"To validate this minimalist approach, the authors tested JAZ against specialized systems without using any pre-built memory or file system tools. The results demonstrate that a simple, code-centric loop can outperform purpose-built frameworks:",[72,8133,8134,8140],{},[36,8135,8136,8139],{},[39,8137,8138],{},"Long-Horizon Recall:"," On the StuLife benchmark, JAZ outperformed Letta (formerly MemGPT) by 8% while operating at half the cost.",[36,8141,8142,8145],{},[39,8143,8144],{},"Continual Self-Improvement:"," On the AppWorld benchmark, JAZ outperformed ACE by 4% at a lower cost.",[22,8147,8148],{},"These results suggest that the current trend of building increasingly complex agent harnesses may be less effective than simply providing LLMs with a clean, recursive interface to interact with their own execution environment.",{"title":154,"searchDepth":155,"depth":155,"links":8150},[8151,8152,8153],{"id":8080,"depth":155,"text":8081},{"id":8091,"depth":155,"text":8092},{"id":8127,"depth":155,"text":8128},[167],{"content_references":8156,"triage":8161},[8157,8159],{"type":174,"title":8158,"context":183},"Letta",{"type":174,"title":8160,"context":183},"ACE",{"relevance":189,"novelty":190,"quality":190,"actionability":162,"composite":8162,"reasoning":8163},4.15,"Category: AI & LLMs. The article introduces JAZ, a minimalist agent framework that simplifies agent architecture, addressing a core topic of AI engineering. It presents new insights into agent design by demonstrating how a single 'invoke' primitive can enhance performance, which is relevant for developers looking to implement efficient AI solutions.","\u002Fsummaries\u002F5c76f9e2f8d51cc7-jaz-a-minimalist-agent-framework-using-code-as-a-h-summary","2026-09-25 03:18:22",{"title":8070,"description":154},{"loc":8164},"5c76f9e2f8d51cc7","arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.26891","summaries\u002F5c76f9e2f8d51cc7-jaz-a-minimalist-agent-framework-using-code-as-a-h-summary",[205,206,207,8173],"ai-tools","JAZ replaces complex, specialized agent harnesses with a single 'invoke' primitive, allowing LLMs to manage memory and self-improvement through recursive code execution.",[],"EcLVFZi_duvv4E-gACT4g2P9q-kHPWHeaEwOVN519k8",{"id":8178,"title":8179,"ai":8180,"body":8185,"categories":8297,"created_at":168,"date_modified":168,"description":154,"extension":169,"faq":168,"featured":170,"kicker_label":168,"meta":8298,"navigation":193,"path":8307,"published_at":8308,"question":168,"scraped_at":8309,"seo":8310,"sitemap":8311,"source_id":8312,"source_name":200,"source_type":201,"source_url":8313,"stem":8314,"tags":8315,"thumbnail_url":8316,"tldr":8317,"tweet":8318,"unknown_tags":8319,"__hash__":8320},"summaries\u002Fsummaries\u002F4761fd53c100980e-implementing-real-time-tool-calling-for-voice-ai-a-summary.md","Implementing Real-Time Tool Calling for Voice AI Agents",{"provider":7,"model":8,"input_tokens":8181,"output_tokens":8182,"processing_time_ms":8183,"cost_usd":8184},5835,709,3385,0.00252225,{"type":14,"value":8186,"toc":8292},[8187,8191,8194,8220,8223,8227,8230,8244,8248,8251,8289],[17,8188,8190],{"id":8189},"the-tool-calling-loop","The Tool-Calling Loop",[22,8192,8193],{},"In a live voice environment, the model cannot directly manipulate application state (e.g., playing music or skipping tracks). Instead, it relies on a four-step loop:",[33,8195,8196,8202,8208,8214],{},[36,8197,8198,8201],{},[39,8199,8200],{},"Declare",": Provide the model with a menu of available functions, including names and descriptions.",[36,8203,8204,8207],{},[39,8205,8206],{},"Decide",": The model emits a tool call based on user input.",[36,8209,8210,8213],{},[39,8211,8212],{},"Execute",": Your backend code runs the corresponding function.",[36,8215,8216,8219],{},[39,8217,8218],{},"Return",": The result is sent back to the model, allowing the conversation to continue seamlessly.",[22,8221,8222],{},"Crucially, the model selects tools based on their descriptions. If a model consistently calls the wrong tool, the primary fix is to refine the function's docstring or description rather than adjusting the model itself.",[17,8224,8226],{"id":8225},"implementation-approaches","Implementation Approaches",[22,8228,8229],{},"There are two primary ways to implement these abilities:",[72,8231,8232,8238],{},[36,8233,8234,8237],{},[39,8235,8236],{},"Raw SDK",": You manually manage the dictionary-based tool definitions and wire the four-step loop yourself. This offers maximum control.",[36,8239,8240,8243],{},[39,8241,8242],{},"Agent Development Kit (ADK)",": You define abilities as standard Python functions with docstrings. The framework automatically parses the schema and handles the execution loop, significantly reducing boilerplate code.",[17,8245,8247],{"id":8246},"rules-for-smooth-interactions","Rules for Smooth Interactions",[22,8249,8250],{},"To prevent the agent from feeling \"broken\" during a live conversation, follow these two rules:",[33,8252,8253,8259],{},[36,8254,8255,8258],{},[39,8256,8257],{},"Tools Must Be Synchronous and Instant",": In a live session, the agent's voice pauses while waiting for a tool to return. If a tool is slow, the silence feels like a system failure rather than a loading state. If an action is inherently slow, return an immediate acknowledgment to the model and perform the heavy lifting asynchronously.",[36,8260,8261,8264,8265,8268,8269],{},[39,8262,8263],{},"Use a 'Before Tool' Checkpoint",": Implement a ",[81,8266,8267],{},"before_tool_callback"," to intercept every tool call before execution. This allows you to:",[72,8270,8271,8277,8283],{},[36,8272,8273,8276],{},[39,8274,8275],{},"Observe",": Log all tool activity in one central location.",[36,8278,8279,8282],{},[39,8280,8281],{},"Block",": Prevent specific actions based on policy (e.g., enforcing \"quiet hours\" by blocking loud music requests).",[36,8284,8285,8288],{},[39,8286,8287],{},"Rewrite",": Modify arguments before the function runs.",[22,8290,8291],{},"This checkpoint is essential for building robust agents, especially when moving toward more complex tasks like browser automation, where you must validate actions before they interact with the real world.",{"title":154,"searchDepth":155,"depth":155,"links":8293},[8294,8295,8296],{"id":8189,"depth":155,"text":8190},{"id":8225,"depth":155,"text":8226},{"id":8246,"depth":155,"text":8247},[167],{"content_references":8299,"triage":8305},[8300,8303],{"type":174,"title":8301,"url":8302,"context":177},"Gemini Live API","https:\u002F\u002Fg.dev\u002Fcloud\u002Fgemini-live",{"type":174,"title":8242,"url":8304,"context":177},"https:\u002F\u002Fg.dev\u002Fcloud\u002Fadk-docs",{"relevance":189,"novelty":190,"quality":190,"actionability":189,"composite":191,"reasoning":8306},"Category: AI & LLMs. The article provides a detailed framework for implementing real-time tool calling in voice AI agents, addressing practical challenges that developers face when integrating AI features. It offers actionable steps, such as using a synchronous tool-calling loop and implementing a 'before_tool_callback', which are directly applicable to building responsive AI products.","\u002Fsummaries\u002F4761fd53c100980e-implementing-real-time-tool-calling-for-voice-ai-a-summary","2026-09-23 16:00:02","2026-09-24 03:20:51",{"title":8179,"description":154},{"loc":8307},"4761fd53c100980e","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Sdrpso7IKtU","summaries\u002F4761fd53c100980e-implementing-real-time-tool-calling-for-voice-ai-a-summary",[205,206,207,8173],"https:\u002F\u002Fi.ytimg.com\u002Fvi\u002FSdrpso7IKtU\u002Fhqdefault.jpg","To build responsive voice agents, use a synchronous tool-calling loop where the model decides and your code executes. Keep tools instant to avoid conversation gaps and use a 'before_tool_callback' checkpoint to enforce policies and handle slow actions.","This video explains how to implement tool calling for real-time voice agents using the [Gemini Live API](https:\u002F\u002Fg.dev\u002Fcloud\u002Fgemini-live) and the [Agent Development Kit (ADK)](https:\u002F\u002Fg.dev\u002Fcloud\u002Fadk-docs). It focuses on maintaining conversational flow by ensuring tools execute synchronously and using a `before_tool_callback` to intercept and validate agent actions before they run.",[],"fSzzPt7Sp8LM2TTj9WPrSvH_7dJVS8BwyL3-M74vtRM",{"id":8322,"title":8323,"ai":8324,"body":8329,"categories":8388,"created_at":168,"date_modified":168,"description":154,"extension":169,"faq":168,"featured":170,"kicker_label":168,"meta":8389,"navigation":193,"path":8404,"published_at":8405,"question":168,"scraped_at":8406,"seo":8407,"sitemap":8408,"source_id":8409,"source_name":8410,"source_type":201,"source_url":8411,"stem":8412,"tags":8413,"thumbnail_url":8415,"tldr":8416,"tweet":8417,"unknown_tags":8418,"__hash__":8419},"summaries\u002Fsummaries\u002F9471ba9f6c044917-recursive-language-models-beyond-context-windows-summary.md","Recursive Language Models: Beyond Context Windows",{"provider":7,"model":8,"input_tokens":8325,"output_tokens":8326,"processing_time_ms":8327,"cost_usd":8328},8189,857,4326,0.00333275,{"type":14,"value":8330,"toc":8382},[8331,8335,8338,8342,8345,8349,8375,8379],[17,8332,8334],{"id":8333},"the-core-shift-from-token-attendance-to-symbolic-execution","The Core Shift: From Token Attendance to Symbolic Execution",[22,8336,8337],{},"Traditional LLM architectures rely on stuffing context windows with data, which leads to \"context rot\" and performance degradation as the window fills. Recursive Language Models (RLMs) fundamentally change this by treating the context as a symbolic object living within a REPL (Read-Eval-Print Loop) environment. Instead of forcing the model to attend to every token simultaneously, the RLM interacts with data as variables. This allows the model to write and execute code (e.g., regex, data frame operations) to slice, compute, and iterate over inputs, effectively offloading the heavy lifting of data processing to deterministic code execution.",[17,8339,8341],{"id":8340},"recursive-decomposition-and-delegation","Recursive Decomposition and Delegation",[22,8343,8344],{},"RLMs enable a recursive problem-solving pattern where a main model can delegate sub-tasks to itself or other specialized models. By defining an objective and a set of inputs\u002Foutputs, the model can decompose complex tasks into smaller, manageable sub-problems. Because these sub-models also operate within the same REPL environment, they only return the relevant results to the main context. This approach avoids the overhead of passing large JSON strings back and forth—a common bottleneck in standard agentic frameworks—and keeps logic, execution, and results tightly coupled.",[17,8346,8348],{"id":8347},"practical-advantages-and-performance","Practical Advantages and Performance",[72,8350,8351,8357,8363,8369],{},[36,8352,8353,8356],{},[39,8354,8355],{},"Efficiency:"," By using code to process data (e.g., querying a data frame directly), RLMs avoid the bloat of traditional tool-calling agents.",[36,8358,8359,8362],{},[39,8360,8361],{},"Scalability:"," RLMs handle arbitrarily long inputs (like 500,000 lines of code or massive invoice directories) without requiring complex chunking or embedding strategies.",[36,8364,8365,8368],{},[39,8366,8367],{},"Accuracy:"," On long-chain-of-thought benchmarks, RLM-based approaches have demonstrated significant improvements, moving accuracy from 2.6% to 45.4% in specific tasks. The gains are most pronounced in logic-heavy domains like chemistry, chess, and code analysis.",[36,8370,8371,8374],{},[39,8372,8373],{},"Autonomy:"," The model decides when to stop and submit a final answer based on a defined iteration limit, allowing it to explore data structures dynamically until it reaches a satisfactory conclusion.",[17,8376,8378],{"id":8377},"implementation-and-future-outlook","Implementation and Future Outlook",[22,8380,8381],{},"RLMs are currently implemented through frameworks like DSPy, Predict RLM, and Fast RLM. These tools allow developers to focus on high-level abstractions—defining the desired intent and schemas—rather than the mechanics of context engineering. The speaker suggests that the next frontier is \"RLM-aware\" models: models post-trained specifically to understand and leverage this recursive methodology natively, which will likely lead to even more efficient and autonomous agentic workflows.",{"title":154,"searchDepth":155,"depth":155,"links":8383},[8384,8385,8386,8387],{"id":8333,"depth":155,"text":8334},{"id":8340,"depth":155,"text":8341},{"id":8347,"depth":155,"text":8348},{"id":8377,"depth":155,"text":8378},[167],{"content_references":8390,"triage":8402},[8391,8394,8396,8398,8400],{"type":174,"title":8392,"url":8393,"context":177},"DSPy","https:\u002F\u002Fgithub.com\u002Fstanfordnlp\u002Fdspy",{"type":174,"title":8395,"context":183},"Predict RLM",{"type":174,"title":8397,"context":183},"Fast RLM",{"type":8038,"title":8399,"context":8036},"Ulong Benchmark",{"type":8038,"title":8401,"context":8036},"Browse Comp Benchmark",{"relevance":189,"novelty":190,"quality":190,"actionability":162,"composite":8162,"reasoning":8403},"Category: AI & LLMs. The article discusses Recursive Language Models (RLMs) and their practical advantages over traditional LLMs, addressing a core audience pain point regarding efficiency and scalability in AI product development. It provides insights into how RLMs can improve performance, but lacks detailed actionable steps for implementation.","\u002Fsummaries\u002F9471ba9f6c044917-recursive-language-models-beyond-context-windows-summary","2026-09-09 13:30:27","2026-09-10 03:11:48",{"title":8323,"description":154},{"loc":8404},"9471ba9f6c044917","AI Engineer","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=xo68uCibfm8","summaries\u002F9471ba9f6c044917-recursive-language-models-beyond-context-windows-summary",[205,206,207,8414],"automation","https:\u002F\u002Fi.ytimg.com\u002Fvi\u002Fxo68uCibfm8\u002Fhqdefault.jpg","Recursive Language Models (RLMs) treat context as a symbolic object in a REPL, allowing models to write code, iterate, and delegate sub-tasks to themselves, effectively bypassing traditional context window limitations and RAG-based bloat.","This is a technical talk explaining Recursive Language Models (RLMs), which move beyond standard RAG or agentic tool-calling by allowing a model to treat its context as a symbolic object within a Python REPL. The speaker demonstrates how this approach enables models to decompose complex tasks, execute code to process data, and delegate sub-tasks to themselves, effectively bypassing context window limitations and \"context rot.\"",[],"a6cs4xCuMXROee0T2oBb5lHJ-aoYKUvJwOB94bO82iE"]