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      <title>Article: The Self-Building Agent: A LangChain4j Experiment</title>
      <link>https://www.infoq.com/articles/self-building-agent-langchain4j/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Programming-articles</link>
      <description>&lt;img src="https://res.infoq.com/articles/self-building-agent-langchain4j/en/headerimage/the-self-building-agent-a-langChain4j-experiment-header-1784637877074.jpg"/&gt;&lt;p&gt;The article discusses an experiment where a code assistant had to design an agentic system using LangChain4j documentation. The assistant created a coding framework capable of writing, testing, and debugging code autonomously. Results showed that two architectural patterns—supervisor and workflow—offered different trade-offs between flexibility and execution speed during debugging tasks.&lt;/p&gt; &lt;i&gt;By Kevin Dubois, Mario Fusco&lt;/i&gt;</description>
      <category>Agents</category>
      <category>Artificial Intelligence</category>
      <category>LangChain4j</category>
      <category>Large language models</category>
      <category>Java</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
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      <pubDate>Fri, 24 Jul 2026 09:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/self-building-agent-langchain4j/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Programming-articles</guid>
      <dc:creator>Kevin Dubois, Mario Fusco</dc:creator>
      <dc:date>2026-07-24T09:00:00Z</dc:date>
      <dc:identifier>/articles/self-building-agent-langchain4j/en</dc:identifier>
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      <title>Article: Comprehension at AI Speed: Building a Context Store for Evolutionary Architecture</title>
      <link>https://www.infoq.com/articles/ai-speed-context-store-architecture/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Programming-articles</link>
      <description>&lt;img src="https://res.infoq.com/articles/ai-speed-context-store-architecture/en/headerimage/ai-speed-context-store-architecture-header-1783673492911.jpg"/&gt;&lt;p&gt;AI makes the first 80% of development feel fast, but hides architectural complexity until it's too late. To prevent system instability, engineering leaders must shift from raw throughput to systemic comprehension. By unifying spec-anchored SDD, TDD, and automated fitness functions into a repo-bound "Context Store," teams can ensure AI agents and human reviewers evolve code safely.&lt;/p&gt; &lt;i&gt;By Stella Berhe, Stephan Bragner, Vikram Maran, Anand Jayaraman&lt;/i&gt;</description>
      <category>Artificial Intelligence</category>
      <category>Governance</category>
      <category>AI Development</category>
      <category>Evolutionary Architecture</category>
      <category>InfoQ Certification Program</category>
      <category>TDD</category>
      <category>Specification</category>
      <category>Architecture ICSAET</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>article</category>
      <pubDate>Tue, 14 Jul 2026 09:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/ai-speed-context-store-architecture/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Programming-articles</guid>
      <dc:creator>Stella Berhe, Stephan Bragner, Vikram Maran, Anand Jayaraman</dc:creator>
      <dc:date>2026-07-14T09:00:00Z</dc:date>
      <dc:identifier>/articles/ai-speed-context-store-architecture/en</dc:identifier>
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