𝗪𝗵𝗲𝗿𝗲 𝗬𝗼𝘂𝗿 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗙𝗮𝗶𝗹𝘀
In 2026, you must understand the agentic loop. It is a core skill.
The loop follows this path: Goal → Plan → Execute → Evaluate → Adjust → Repeat
The agent does not stop after one response. It runs until it meets the goal or hits a wall.
Most engineers miss a key fact. Every phase has a way to fail.
The Plan Phase Errors start here. A bad plan creates a perfect sequence of steps for the wrong problem. Review every plan before you let the agent run.
The Execute Phase The agent takes real action. It writes files and runs commands. You need reversibility. Use Git commits at checkpoints. Use staging environments. Always have a rollback path.
The Evaluate Phase The agent checks its own work. It catches obvious mistakes. It misses subtle errors because it uses the same logic that created the mistake.
The Adjust Phase A good agent fails loudly. If an agent stays silent when it should report progress, investigate it. Silence is a signal.
You also need to manage knowledge. Models have training cutoffs. They rely on old data.
Use RAG (Retrieval-Augmented Generation) to fix this. RAG pulls current documents during the query. It makes the agent use real, fresh data instead of old memory.
Claude Opus 4.6 offers a 1 million token context window. You can put your entire codebase in the context. This makes repository-level reasoning possible.
Tomorrow, I will talk about what AI does to engineering jobs.
Optional learning community: https://t.me/GyaanSetuAi