# Agentic Engineering: 5 Terms Every AI Developer Should Know

## Executive summary

Agentic Engineering describes the shift in software development where AI models perform complex tasks autonomously. The video details five critical concepts—Prompt, Context, Harness, Loop, and Graph Engineering—that govern how AI agents are designed, managed, and integrated into the Software Development Lifecycle (SDLC), enabling full automation from task management to code implementation.

## Key takeaways

- AI-Assisted Development is the New Norm: AI coding assistants are already writing a significant portion of code (80% or more) in large teams, making understanding agentic engineering essential for modern development practices.
- Communication is Key (Domain Expertise): The ability to communicate requirements clearly (domain expertise) is more critical than the raw capability or size of the LLM itself.
- Systemic Automation Requires Multiple Layers: Achieving robust automation requires combining techniques like context management (Context Engineering), execution control (Harness Engineering), and workflow orchestration (Loop/Graph Engineering).

## Technical details

- Prompt Engineering: The practice of crafting precise instructions for an LLM. For smaller, local models, this often involves providing 'few-shot prompts' (examples of Q&A) and 'Chain-of-Thought (COT)' examples to guide the model's reasoning.
- Context Engineering: Managing the entire input window of the model. Techniques include summarizing or compacting the conversation history, utilizing sub-agents (which perform tasks like research or documentation lookup without overloading the main context), and managing information pulled via Retrieval Augmented Generation (RAG).
- Harness Engineering: The layer surrounding the model that enables action and verification. It provides the runtime environment to execute commands (e.g., editing files), manage context, and enforce security via sandboxing. It allows agents to interact with external tools and utilities, such as CI pipelines, and provides feedback (e.g., checking if a test suite passed).
- Loop Engineering: The concept of an autonomous agent loop, where the agent acts using tools, observes the output, and decides whether to continue or stop. Loop Engineering formalizes this by attaching a trigger (e.g., a cron job, a pull request opening, or a bug alert) to orchestrate the agent's actions.
- Graph Engineering: An extension of the loop concept that ties multiple agents together in parallel. Multiple specialized sub-agents (e.g., one for market research, one for repository structure analysis) can pass off their work to a final implementing agent.
- Software Factories: The ultimate goal: automating the entire SDLC. This involves setting up robust pipelines using individual, task-based agents, each requiring components like isolation (sandboxes/containers), identity (access control, e.g., Git), and observability (tracing all actions).

## Practical implications

- Build engineers must design pipelines that incorporate feedback loops and verification steps (Harness Engineering) to ensure AI-generated code is functional and secure.
- The integration of CI/CD pipelines with AI agents is critical, as agents must be able to run tests and detect merge conflicts autonomously.
- Designing modular, task-specific agents (Software Factories) is superior to relying on single, general-purpose LLMs for complex SDLC tasks.

## Topics

Agentic Engineering, AI Agents, Prompt Engineering, Context Management, Software Development Lifecycle (SDLC), Automation, CI/CD, Learn more about Agentic Engineering, AI updates newsletter

Source: https://www.youtube.com/watch?v=SIZsFGvtClM