In the near future, every company will be supported by a wide range of AI-powered tools.
Agents for communication, procurement, HR, sales, and finance will automate tasks, optimize processes, and free up time for strategy, innovation, and customer relationships.
Great at tasks. Blind to the law.
These agents will transform how we work. But as workflows speed up, so do legal and regulatory risks. In this fast-moving environment, companies will face challenges that traditional tools and legal services simply can’t keep up with.
That’s why we’re building the first ambient AI-Lawyer!
Designed specifically for the legal reality of SMEs, the AI-Lawyer bridges the gap between automation and legal certainty — enabling companies to move fast while staying legally secure.
Our AI-Agent adds a layer of legal intelligence to new and existing workflows.
It reviews, warns, documents, and advises — not someday, but in real time. Think of it as an automated legal department for every company that can’t afford one.
Cross-checks support requests against internal policies and legal obligations — ensuring legally sound responses.
It quietly embeds itself into your workflows, tools, and routines, bringing legal awareness where there was none before.
Our first Legal Agent, Dieter Datenschutz, already replaces manual GDPR services.
All documents at the click of a button (Privacy Policies, DPAs, TOMs…)
All processes automated (DPIAs, record-keeping, audit logs…)
Concrete guidance on every compliance step, from data mapping to breach response
Start with Dieter and join the waitlist.
Test Dieter now and join our waitlist for the full Ambient AI-Lawyer experience - so you’ll never worry about GDPR again.
On-demand advice
Your legal cockpit
Document wizard
>
Registered Users
of SMEs in Germany
struggle with legal uncertainty
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Legal documents drawn up
Nr. 2 compliance app, 4.9 of 5 stars
The first AI-Agent for GDPR compliance
Built for SMEs. Easy to use. Solves your Problem in minutes
Proven expertise in law, product development and machine learning.
Sebastian Schenk, CEO
Sebastian combines legal expertise with hands-on process knowledge, making sure our AI-lawyer solves real problems – not just legal theory.
Mario Kunze, CPO
With a background in both design and business, Mario turns legal complexity into intuitive accessible tools – helping users get it right without thinking like a lawyer.
Martin Hanewald, CTO
As a seasoned data scientist, Martin turns legal logic into AI workflows – enabling our agent to reason, act, and improve with every request.
Franziska Breidenbach, Legal Lead
Franziska ensures that every legal output – from documents to decision – meets professional standards of accuracy, consistency, and compliance.
Agentic AI systems represent a radical shift from traditional AI applications. While conventional large language models (LLMs) respond purely reactively to inputs, agentic systems proactively take responsibility: they show initiative, make decisions, and autonomously control complex processes.
Three Core Capabilities of Agentic Systems
Agentic LLMs are based on a new AI paradigm. They are characterized by the following key competencies:
Reasoning
They analyze complex problems, evaluate options, and independently develop solution strategies.
Acting
They actively interact with digital or physical environments through interfaces and tools – whether via API calls, automations, or system-level interactions.
They communicate effectively with humans and other agents to exchange information and achieve goals.
These capabilities enable agents to behave adaptively and contextually – a decisive difference from traditional rule-based systems with rigid workflows.
Architecture of Agentic AI Systems
A mature agentic AI system typically consists of the following modules:
LLM Core
Processes natural language and generates semantically precise responses.
Decision Engine
Evaluates context, draws conclusions, and plans actions.
Memory System
Stores short- and long-term information for dynamic learning and contextual understanding (e.g., vector databases like Pinecone/Weaviate, JSON logs, or in-memory cache).
Tool Integration
Enables access to external resources and systems via APIs.
Autonomy Framework
Manages task execution, plans workflows, and responds to environmental changes.
Ethics and Safety Lawyer
Ensures responsible behavior and mitigates potential risks. A monitoring dashboard captures metrics such as API calls, error rates, and execution duration; an audit log records all agent decisions.
Four Core Design Patterns from Our Practice at Simply Legal
In developing and applying agentic AI, four proven design patterns have emerged at Simply Legal:
Reflection Pattern
The agent critically evaluates its own decisions and outputs before acting. This pattern creates a feedback loop for continuous learning and optimization.
Tool Use Pattern
Agents dynamically extend their capabilities by using external tools – for example:
Querying data via web APIs
Analyzing structured data
Executing complex scripts
Integrating specialized ML models
Communication occurs via protocols such as the Model Context Protocol (MCP).
Planning Pattern
The agent breaks down large tasks into logical sub-tasks, identifies dependencies, and orchestrates parallel processes.
Multi-agent Pattern
A network of specialized agents takes on different roles – such as research, analysis, or decision-making. Coordination occurs through agent-to-agent protocols (A2A), ensuring a streamlined flow of information.
Comparing communication and integration approaches
Conclusion
Agentic AI systems are more than just an evolutionary step – they form the foundation of a new AI era. Their ability to act autonomously, interact, and leverage tools makes them powerful players in digital ecosystems. Those who understand their potential and use it strategically can gain a lasting competitive edge. Be prepared to evaluate and integrate agentic concepts over the next 12–24 months to remain competitive.