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Agentic Coding: Working With Claude Code, Codex, and Cursor

Coding agents are now the default way software gets written. Learn the gather-act-verify loop, how to write CLAUDE.md and AGENTS.md files that actually steer an agent, when to use skills and subagents, and how to review agent output like a senior engineer.

#coding-agents#agents#agent-engineering#python

Context Engineering: Designing What the Model Sees

The context window is a budget, and everything competes for it: the system prompt, the tool list, retrieved documents, memory, and the conversation so far. Learn to design the context deliberately, scope tools per task, compact without losing what matters, and treat cache hit rate as the metric it has become.

#context-engineering#agent-engineering#prompt-caching#agent-memory#rag#llm

Harness Engineering: The Runtime Around the Model

Agent = model + harness. The harness is the deterministic runtime that validates, authorizes, executes, and logs every action the model proposes. Learn its five layers, build one from scratch, and adopt the loop that turns every agent failure into a permanent fix.

#harness-engineering#agent-engineering#agents#durable-execution#guardrails#system-design

MCP and Agent Protocols: Building and Consuming Tool Servers

The Model Context Protocol is how agents connect to tools, data, and prompts, and every major provider supports it. Build an MCP server in Python, design tools a model will use correctly, connect it to a coding agent, and learn where A2A and the other agent protocols fit.

#mcp#a2a#agent-engineering#tool-use#agents#python

Reasoning Models, Post-Training, and Test-Time Compute

Where reasoning models come from, what they cost, and when to pay for them. The SFT to DPO to GRPO pipeline, reinforcement learning with verifiable rewards, test-time compute as a dial, and a method for deciding which of your requests deserve a thinking model.

#reasoning-models#post-training#rl#fine-tuning#llm#model-routing

Agent Evals: Trajectories, Tool Calls, and Regression Suites

An agent produces a trajectory, not an answer, and grading only the final answer misses looping, wrong tools, and failure to recover. Build a task suite from real failures, grade tool-call correctness programmatically, validate an LLM judge, and wire it all into CI as a regression gate.

#agent-evals#evaluation#agent-engineering#agents#cicd
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