Mastering Agentic Workflows – 20 Principles to Build Smarter AI Systems

20 Principles for Building Smarter Agentic AI Systems

Summary: Building reliable AI agents requires five disciplines: evaluation, context engineering, prompt engineering, tool design, and workflow orchestration.

Context is everything an LLM has access to when generating a response — prompts, conversation history, tool outputs, retrieved data. It functions as the model’s working memory.

Evaluation-Driven Development

Define success metrics and test cases before building, so progress is measured with data, not intuition.

1. Metrics — Track accuracy/pass rate, processing time (TTFT, latency), and system throughput.
2. Test Case Design — Start with core “happy path” scenarios, then use LLMs to generate edge cases and adversarial inputs.
3. Embrace Uncertainty — LLM outputs are non-deterministic; use an LLM-as-judge for semantic evaluation instead of exact-match checks.

Context Engineering

Deciding what information enters the context window, and how it’s structured, so every token counts.

4. Art and Science — Coined by Tobi Lütke, expanded by Andrej Karpathy: filling the context window with exactly the right information for the next step.
5. Start With Intent — Only include context that serves the model’s next decision or action.
6. Curate Relevant Information — Include the minimal data needed: task data, prior summaries, tool results, tool descriptions.
7. Design for Stages — Early steps need identity/goals; middle steps need intermediate results; late steps need summaries and validation.

Prompt Engineering

Structuring and phrasing curated context so the model interprets it correctly.

8. Version Control Prompts — Track prompts like code to measure how changes affect behavior.
9. Keep It Simple (KISS) — Minimal, well-structured prompts (e.g., XML tags) reduce ambiguity and token cost.
10. Guidelines Over Examples — Modern models need clear rules more than few-shot examples, which can consume context or mislead the model.
11. Avoid Conflicting Information — Instructions across prompts, tools, and docs must point in one direction.
12. Make the Agent Workflow-Aware — Explicitly describe what each tool does and when to use it.

Bonus: Write system prompts and tool docs in English, even for multilingual apps — most LLMs reason best in English.

Tool Design

Tools let agents retrieve information or take action in the real world.

13. Name It, Document It, Return It — Clear verb-based names, honest documentation, natural-language outputs.
14. Treat the LLM as a Flow Composer — Scope tools at a middle level: not too granular, not one giant function. Deterministic steps belong inside the tool; judgment belongs to the LLM.
15. Be Conversational About Errors — Return failures in plain language so the agent can recover, rather than failing silently.
16. Tools Can Be Anything — A tool can wrap another model (OCR, image generation, a classifier).
17. Do You Really Need Tools? — Have the model check existing context before calling a tool, to cut redundant calls.

Agentic Workflow

Wiring prompts, tools, and context into a system that reasons and acts over time.

18. The Agent Loop — The core “think → act → observe” cycle (ReAct pattern), repeated until the task is done.
19. Human in the Loop — Pause for human confirmation before sensitive actions like payments or messages.
20. Memory Matters — Use tiered memory: short-term (recent actions), mid-term (session notes), long-term (external vector stores).

Single-Agent vs. Multi-Agent

Choosing a Model

Reasoning models suit complex, multi-step tasks; standard LLMs suit simpler ones. Larger models perform better but cost more; smaller models work well with strong prompts and context. Prompting/context design should be tried before fine-tuning. Non-LLM models (classifiers, OCR, ASR) often outperform LLMs on narrow tasks.

Conclusion: Agent performance comes from how a system is built around the model — through disciplined evaluation, context, prompts, tools, and workflow design — not from the model alone.