6 Principles of AI Agent Development Established Globally in One Week

The moment 2026 began, Context Engineering became the hottest topic in the AI world.

The conclusion is clear: if you’re building AI agents and you don’t know these principles, you’ll fall behind in 2026. Here’s a distillation of what the global AI community established in just one week.

Operate Context Dynamically, Not Statically

The era of static context is over.

• Manus: Uses the file system as externalized memory, keeping only URLs and paths while restoring full content on demand. KV-Cache hit rate is the core metric
• Cursor: Introduced Dynamic Context Discovery, syncing MCP tool descriptions into folders and cutting token usage by 46.9%
• Context7: Server-side reranking reduced context tokens by 65%, latency by 38%, while actually improving output quality

When your context is a living, breathing system that loads and unloads information as needed, you stop paying for tokens you don’t use - and the model stays focused on what matters.

Planning Is Everything

Agents that receive vague instructions and immediately start executing will fail.

• Claude Code’s AskUserQuestionTool: Interviews the user like a consultant, asking targeted questions to maximize requirement clarity before writing a single line of code
• Plan Mode: Writes a plan to a markdown file before execution. 80% of the outcome is determined at the planning stage

The best AI-assisted code doesn’t come from better prompts. It comes from better plans.

Design Tools Around Bash and Code Generation

Before building custom tools, consider Bash and Codegen first.

• Bash: Composable, lightweight on context, and gives instant access to existing software - ffmpeg, jq, grep, and thousands more
• Codegen: API composition at its core. Ask for the weather and the agent writes a script that calls the Weather API directly

The trade-off: Custom Tools (stable, high context cost) vs. Bash (composable, requires discovery time) vs. Codegen (flexible, longer execution time).

Embrace the Loop

Don’t expect perfect results on the first pass.

• Claude Code’s Ralph Wiggum skill and Recursive Language Models (RLM): Maximizing self-correction loops is the key to quality
• The more verifiable the task, the better this works. If you can validate the output, you can iterate toward perfection

Single-shot prompting is a trap. The real power of AI agents emerges when they’re allowed to try, fail, evaluate, and try again.

Adopt a Multi-Model Strategy

Trying to solve everything with a single model is inefficient.

• Claude Opus 4.5: End-to-end planning and complex development
• Gemini 3 Pro: Frontend implementation, large-scale document processing
• GPT-5.2: Debugging and abstract reasoning
• Route sub-agents to the optimal model per task for both speed and specialization

No single model excels at everything. The winning strategy is model routing - matching each task to the model best suited for it.

Manage State with Layered Memory

Task progress and errors must be managed systematically.

• Manus’s todo.md: Repeatedly inserts goals at the end of context to solve the “lost-in-the-middle” problem
• Memory separation: Short-term (working context), medium-term (session history), long-term (file system)
• Retaining failed actions and stack traces prevents the model from repeating the same mistakes

Without structured memory, agents drift. With it, they compound knowledge across sessions.

The Takeaway

In 2026, we’ve moved beyond agents that merely answer questions or automate simple workflows. We’re now building agents that perform real, complex work.

These six principles are already validated in production by Manus, Cursor, and Claude Code. If you don’t apply them, your competitors will.