Learn the Underlying Terminology

ASE is based on a small set of precisely defined terms. They are layered: each definition builds only on the terms defined before it, from Software and Engineering up to Agentic Software Engineering itself.

1Software
The collection of programs (instructions) and their associated data that make a computer perform tasks.
2Software Agent
Software that acts autonomously on behalf of a user or another program to achieve delegated goals. Exhibits reactivity (responds to environment), proactivity (goal-directed initiative), and social ability (interacts with other agents/users). Perceives its environment via sensors/inputs (APIs, files, messages, events) and acts via actuators/outputs (commands, calls, messages).
3Engineering
The systematic application of scientific and technological knowledge, principles, approaches, practices, methods, and experiences to design, build, operate, and maintain solutions, up to complex production-grade ones, through a traceable process.
4Software Engineering
The application of Engineering to the development, operation, and maintenance of Software.
5Artificial Intelligence (AI)
The field of Computer Science focusing on building systems that perform tasks normally requiring human intelligence: perception, reasoning, learning, planning, decision-making, and language understanding. Spans Symbolic AI (rules, logic, search) and Machine Learning (statistical pattern learning from data), including Deep Learning (neural networks) and, within it, Generative AI (e.g. Large Language Models, or LLMs, and diffusion models). Operates along a spectrum from narrow AI (task-specific, e.g. image classifiers) to hypothetical Artificial General Intelligence (AGI) with general, human-level capability.
6Agent Harness
The Software that surrounds an AI model and turns it from a passive, predicting model into an acting Software Agent. It implements a control loop, like ReAct (Reasoning and Acting) or Plan-and-Execute, by sending context to the model, interpreting its responses, executing requested actions, and feeding results back. It also supplies everything the model cannot provide itself: the tools that act as sensors and actuators, the assembly and management of context, and the guardrails. The model decides what to do; the harness determines what it can do and how it is carried out.
7AI Agent
A Software Agent formed by an AI model (typically an LLM) as its reasoning core, operated by an Agent Harness.
8Agentic AI
The paradigm of building AI systems out of one or more AI Agents that perceive, plan, decide, act, and adapt to pursue high-level, long-horizon goals with a high degree of autonomy, instead of just answering single prompts. Typically combines multiple specialized AI Agents, coordinated through task decomposition, orchestration, and shared context. Distinct from purely generative (prompt-response) AI by the fact that its systems take actions in the world (call APIs, run code, edit files, browse the web).
9Agentic Software Engineering
Software Engineering following the Agentic AI paradigm, in which AI Agents perform multi-step tasks across the software development lifecycle, within a traceable process. Goes beyond inline code completion and chat assistants. Engineers shift toward goal specification, oversight, review, and governance, with human-in-the-loop gates.

Understand Our Philosophy

ASE is based on Dr. Ralf S. Engelschall's Agentic AI Level model, which explains the distinct modi operandi of working with AI agents. ASE allows engineers to stay in the driver's seat and to decide the particular modus operandi per task themselves. In practice, this choice is usually based on the risk one can accept for the currently involved piece of software.

Agentic AI Levels

From Manual To
Fully Autonomous

ASE supports Dr. Ralf S. Engelschall's Agentic AI Level Model, which classifies the gradual sharing of work with AI agents.

Level 0 (manual): you do everything manually and without support from the AI agent at all — think "do-it-yourself", where you obviously know the results and inherently train your own skills.

Level 1 (assisted): you are supported by the AI agent for individual steps only — think "assistant", where you still know all results in detail, but inherently your practical execution skills can partly start to wane in the long term.

Level 2 (supervised): you are working on an entire task with the AI agent as an equal pair — think "co-worker", where your colleague shares the task workload with you, you mutually review each other's results, hence you still know all results in detail, but inherently your practical execution skills can be lost in the long term.

Level 3 (delegated): you are delegating entire workflows to the AI agent — think "offshoring", where your colleague shoulders the entire workload for you, you just review the results, but your own practical execution skill and your ability to quality-assure the results will be forever lost if you are not regularly, at least virtually, replaying the delegated work.

Level 4 (autonomous): you are assigning entire process lifecycles to the AI agent — think "third-party", where your vendor does everything for you fully autonomously, you just take the results as is, and your own practical execution skill doesn't matter as it is already out of scope.

Understand Our Design Assumptions

ASE is based on the following assumptions which inherently drive whether ASE is for you or not:

  • You are in the Driver's Seat:
    ASE assumes you are an experienced Software Developer or even Software Architect and want to still be in the driver's seat, i.e., you decide and trigger the next operations and you are reviewing the results. This means ASE is neither about plain "vibe coding" nor driving fully autonomous Software Engineering agents.
  • Thinking before Acting:
    ASE assumes you are an engineer, which means that you prefer thinking long enough instead of immediately acting to increase the overall result quality and at least avoid unnecessary post-adjustments to your results.
  • Flexible Operation Modes:
    ASE assumes your job as a Software Developer and Software Architect inherently requires multiple operation modes, ranging from ad-hoc LLM prompting, through a tenet-honoring direct mode, through a user-story-based task mode, up to an artifact-reconciling sync mode. You are free to choose per task the mode which fits best.
  • Unix CLI-style Skills:
    ASE assumes you like the style of the Unix command-line interface (CLI) and appreciate skills which are intended to be explicitly called as commands and which can be parametrized through Unix-style options.

Know Our Design Decisions

ASE is based on the following distinct decisions which inherently shaped the overall design of ASE:

  • Agent & Plugin:
    ASE is a real plugin for the Agentic AI Coding tools Anthropic Claude Code CLI, GitHub Copilot CLI, and OpenAI Codex CLI, and can be non-intrusively and easily installed and later also residue-free uninstalled from those tools at any time.
  • Recurring Software Engineering Tasks:
    ASE targets the most important, recurring tasks in industrial Software Engineering only. Especially, ASE is not targeting the Consulting, Operations, or Management disciplines.
  • Built-In Methodology:
    ASE tries to ship out-of-the-box with built-in well-known methodology aspects to make them more accessible to the average software developer and software architect. See below under Trust Built-In Methodology for more details on the actually built-in methods.
  • Skills & MCP/CLI:
    The probabilistic LLM-based ASE skills are strongly coupled to and work on top of the deterministic TypeScript-based ASE MCP/CLI service. In particular, the ASE skills are not written to be used standalone or in foreign environments.
  • Configuration Scopes:
    Parameters of the agent and the project can be configured across the hierarchy of the distinct scopes default, user, project, task, and session. This allows the flexible configuration of ASE.
  • Session Constitution:
    All agent sessions have meta descriptions — a sort of "constitution" — preloaded at all times, based on the configured parameters. This allows controlling the general LLM behavior. Additionally, skills load more meta descriptions on demand. This allows skills to reuse definitions.
  • Task Skills:
    Recurring tasks are supported with dedicated skills, which can be manually triggered as commands. This allows explicitly controlling the specific agent behavior. Skills are grouped into meta (ase-meta-*), code (ase-code-*), architecture (ase-arch-*), task (ase-task-*), documentation (ase-docs-*), repository (ase-repo-*), synchronization (ase-sync-*), and help (ase-help-*) families.
  • Artifact Formats:
    The format of the primary input artifacts of Software Engineering (requirements specification and architecture description) is strictly defined. This allows both humans and agents to operate on them concurrently.

Trust Built-In Methodology

One of the strengths of ASE is that it bakes many classic, well-known methods directly into its skills. This way the skills under the hood are rather powerful. Click any of the methods to open the man-page of the skill implementing it.

Overview of the Architecture

ASE is not just a bunch of skills for Agentic AI Coding tools like Anthropic Claude Code CLI. Instead, ASE is based on an integrated set of Hooks, Skills, a Model-Context-Protocol (MCP) service and a Command-Line Interface (CLI).

Software Architecture

Hooks, Skills, MCP, CLI

At first glance, ASE is just a plugin of skills for the Agentic AI Coding tool. Actually, it is more.

First, ASE uses Hooks of the Agentic AI Coding tool to load its constitution (important global information). Then it provides the various user-facing skills (ase-xxx-xxx) for the probabilistic execution of actions. These in turn share much meta information.

Additionally, ASE uses an underlying (automatically started per project) MCP service (ase_xxx tools) for deterministic execution of actions. Finally, ASE provides CLI commands (ase xxx).

npm install -g @rse/ase && ase setup install