6 Principles of AI Agent Development Established Globally in One Week

At the start of 2026, context engineering became the dominant topic in AI agent development. Within roughly one week, practitioners at Manus, Cursor, and the Claude Code team converged on a set of principles that are now showing up across production systems. What follows is a distillation of those six principles, along with where each one still has real limits.

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% and latency by 38%, while improving output quality.

When context loads and unloads information as needed, the model stays focused and token costs fall. The difficulty is that dynamic loading introduces retrieval failures: if the wrong content is pulled at the wrong moment, the agent silently works from incomplete information. Getting retrieval right is harder than it looks.

Planning Before Execution

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 does not 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 is real: custom tools are stable but carry high context cost; Bash is composable but requires discovery time; Codegen is flexible but has longer execution time. There is no universally correct choice; the right option depends on how often the task repeats and how much context budget you have.

Embrace the Correction Loop

Do not 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 output quality.
• The more verifiable the task, the better this works. If you can validate the output, you can iterate toward a correct result.

Single-shot prompting is a trap. The real power of AI agents emerges when they try, fail, evaluate, and try again. But this only works when the validation step is reliable: an agent correcting against a flawed test suite will confidently produce wrong answers.

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 and 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, and routing adds real coordination overhead, but at the scale where this matters, the specialization gains outweigh the complexity.

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 practical challenge is that memory systems add their own failure modes (stale entries, conflicting state across layers) and most production teams are still figuring out the right hygiene rules.

Where These Principles Stand Now

These six principles are validated in production by Manus, Cursor, and Claude Code. They also represent the current state of a fast-moving field, not a final answer. Each principle has known edge cases, and the teams that developed them are still iterating. In 2026 we are building agents that perform real, complex work, which means the failures are real too, and worth paying as much attention to as the successes.