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Master Claude API integration for enterprise applications. Learn authentication, streaming responses, tool use, vision capabilities, and best practices for building reliable AI-powered features with Anthropic's most advanced models.
Jishu Labs
CTO
January 20, 2026
13 min read
Featured Article
12 articles tagged “ai agents”
Clear tag filterPrompt injection is when untrusted text reaching a model's context gets treated as instruction rather than data. It has held OWASP's number one LLM risk slot across every edition, and agents made it materially worse.
Chat history is not memory. A memory layer is durable, retrievable state about decisions, preferences and facts that survives past the context window. What belongs in one, and what should stay in a log.
Prompt wording stopped being the bottleneck around 2025. The harder problem — deciding what lands in the context window at every turn — got a name. What context engineering is, and the four things it covers.
Traditional observability tells you an agent responded. It does not tell you whether the response was correct. What to instrument for agentic systems, and how tracing and evaluation fit together.
Benchmarks say your agent works; production says otherwise. A practical evaluation approach covering end-to-end, trajectory and component scoring, and how to build an eval suite from your own failures.
Multi-agent architectures are the default recommendation of 2026 and the wrong answer for most teams. A decision framework for choosing between one agent, a planner-executor split, and a full orchestration graph.
A working MCP server in five steps — choosing what to expose, defining tools the model can actually use, handling authorisation, testing against a host, and the mistakes that make a server unusable in practice.
MCP is an open standard that lets an AI model call your tools and read your data through one interface instead of a bespoke integration per vendor. Here is what it is, what it is not, and when it earns its place.
Learn how to build production AI agents for enterprise automation. Covers agent architectures, LLM selection, tool integration, security governance, and ROI measurement for agentic AI systems.
Learn to build AI agents that can reason, plan, and execute complex tasks autonomously. Covers agent architectures, tool use, memory systems, and production deployment patterns.
Master LangChain for building production-ready AI applications. Learn chains, agents, memory systems, RAG pipelines, and deployment strategies with practical code examples for real-world use cases.
Discover how AI agents are revolutionizing software development and business automation. Learn to build autonomous AI systems that can reason, plan, and execute complex tasks with minimal human intervention.
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