Tag
agents
11 articles tagged with “agents”
Building Reliable AI Agents with Semantic Kernel
Plugin architecture, memory, planners, and error handling patterns for building production AI agents in .NET with Semantic Kernel.
MCP Servers: Building Tool-Using AI Agents with the Model Context Protocol
How MCP works, how to build MCP servers that expose tools to AI agents, and practical patterns for connecting LLMs to your systems.
From Chatbot to Agent: Adding Tools, Memory, and Planning to a Simple Chat Interface
A practical walkthrough of evolving a basic LLM chatbot into a capable agent — adding tool calling, persistent memory, and multi-step planning.
Function Calling and Tool Use: Structured Outputs from LLMs
JSON mode, tool schemas, parallel tool calls, and the architecture patterns that let LLMs interact reliably with external APIs and databases.
ReAct Prompting: Reasoning and Acting in LLM Agents
The Thought → Action → Observation loop that lets LLMs use tools, verify intermediate steps, and self-correct — the pattern behind most modern AI agents.
Episodic Memory: Agents That Learn From Experience
Store and retrieve past agent episodes — full trajectories of thought, action, and outcome — so agents avoid repeating mistakes and replicate past successes.
LangGraph: Stateful Agentic Workflows
Model agent workflows as stateful graphs — nodes execute LLM calls or tools, edges control flow — for reliable, debuggable, and resumable agents.
Long-Term Memory for AI Agents
Persist agent knowledge beyond the context window — vector stores, key-value caches, and structured stores for cross-session recall.
Designing Reliable Planning Loops
Build reliable thought-action-observation cycles that let agents decompose complex goals into executable steps without going off-track.
Managing Short-Term Memory in Agents
Manage the context window as working memory — what to include, summarise, or drop to keep agents coherent across long conversations.
Building a Production Tool Registry
Design and maintain a central catalogue of agent tools — versioning, capability tagging, and dynamic discovery for scalable agent systems.