Architecture for Agentic Engineering
It’s not just new workflows or tools that decide whether Agentic Engineering succeeds, but whether your architecture and codebase are actually understandable to agents in the first place. Agents are only as good as the context they get. We help development teams prepare architecture knowledge, design decisions and coding standards so that agents can genuinely work with them. This is complemented by the development of suitable agent harnesses and the organization of agentic development processes. This way, we ensure that AI-assisted development can be used effectively and at scale.
Agentic Engineering at a Glance
Context Engineering for Agents
We help prepare architecture decisions, design principles and coding standards so that LLMs and agents can work with them. The result is living knowledge bases (e.g. CLAUDE.md, architecture context files, knowledge graphs) that the team maintains together and that grow with the system, instead of going stale after the first sprint.
Harness Engineering & Development
Agent harnesses ensure the consistent, successful handling of recurring tasks in everyday development. This includes prompt templates, pipelines for spec-driven development, and prompt and skill libraries.
Agentic Development Processes
Agentic coding only works in the long run if it is anchored as a team process, through clear agent rules, guardrails, review processes and a shared toolchain. We support development teams in building these structures and establishing professional development processes.
Our Offering
Our Agentic Engineering offering is modular – from a compact kick-off to long-term support for your development organization. Feel free to reach out if you have your own ideas or different priorities.
00
Kick-Off & Baseline Assessment
In a half-day workshop, we form a shared picture together: Which agents and tools are in use? Where is structure missing? Where is knowledge getting lost? Based on this, we jointly define where context engineering, harness engineering or process organization will have the greatest leverage.
01
Architecture Context Engineering
We help prepare your system’s architecture knowledge so that agents can understand it and developers can maintain it together. Concretely, this results in things like architecture context files, CLAUDE.md structures and knowledge graphs tailored to your tech stack, your design principles and your quality goals. We also establish a process for keeping this knowledge up to date.
02
Prompt Engineering & Team Library
Together we develop structured prompts, skills and commands for your team’s most common tasks, from code reviews and architecture checks to test generation and documentation. The goal is to distribute these artifacts across the whole team and establish them so that everyone on the team can use and further develop them.
03
Agentic Processes & Guardrails
We support the build-up of agentic development processes, from the toolchain decision through agent rules and guardrails to review processes for AI-generated code. This increases both productivity and safety when using coding agents.
04
Sustainable Support
We support your organization over several weeks or months. In review sessions and team workshops, we reflect on experiences, update the architecture context, sharpen prompts and adapt processes, establishing a culture in which AI-assisted development adapts along with your company, your teams and your goals.
AI Transformation Journey
Placement in the Journey
This service is part of the embarc AI Transformation Framework. The highlighted tile shows where in the journey it fits in – and which other building blocks complement it well.
01
Orientation & Exploration
Outcome: Shared vocabulary, first experience, prioritized use cases
Building Knowledge
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Value Hypotheses, Risk Identification
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Safe Experimentation Spaces, Creating Guardrails
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02
Team Pilots & Validation
Outcome: proven usage patterns and a solid state of practice for scaling
Measurable Value
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Evaluating Development Practices, Agentic SDLC
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Reference Use Cases, Deployment in Real-World Environments
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03
Platformization & Controlled Scaling
Outcome: A "Golden Path" for teams, on which agents can work within clear boundaries.
Architecture, Quality & Observability
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Professionalization, Security, Governance & Compliance, AI-FinOps
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AI Development Platform (Context, Harness, Hooks and Processes)
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04
AI-Native Product Organization
Outcome: A new "AI Engineering Operating Model" for the learning, AI-native product organization.
New Role Models, Skills & Career Paths
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New Organizational Structures: Teams, Communities, AI Hubs
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Intent-Orientation and Outcome-Based Steering (incl. Continuous Validation)
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