AI Agents for Business-Critical Processes
Document Agents. Workflow Agents. Knowledge Agents.
Specialized AI agents that read documents, orchestrate processes, and deliver context-based answers from enterprise knowledge. Every agent operates within a Decision Layer that makes every decision transparent, auditable, and traceable.
Building an agent is easy. Making it enterprise-ready is not.
Workflow agents can be prototyped in days. ChatGPT, Claude, or Llama -- an agent that reads documents and generates answers is no longer a technical challenge.
The real problem starts afterward: Is the agent's decision auditable? Does it meet EU AI Act requirements (transparency, human oversight, recording obligations)? Can the works council trace the rules the agent follows? Does the architecture withstand a SOC 2 audit?
That is exactly where we come in. Not building the agent itself -- but the governance layer that makes it production-ready, auditable, and certifiable. If you plan governance after the PoC, you build twice.
The Core Problem in Enterprises
Enterprise processes rely on the implicit knowledge of individual employees. Collective agreements, works agreements, posting logic, compliance rules -- a complex rule set whose application varies from person to person.
The consequences: inconsistent decisions across locations, errors that only surface during audits, knowledge loss during staff turnover, and processes that do not scale because they depend on individual people.
Decision Quality, Not Process Automation
Enterprise decisions are formally human, but often inconsistently documented. An AI Agent does not replace domain decisions. It structures them, documents them, and makes them reproducible. The goal is not automation for its own sake, but consistent, traceable decision quality across all locations and case workers.
Three Agent Types
1. Document Agents
Document Agents read, understand, and process documents with genuine language comprehension. No template recognition, no rigid OCR rules -- contextual understanding of content.
What they process: incoming invoices and credit notes, sick notes and medical certificates, employment contracts and amendments, certificates and attestations, receipts.
Document → Agent reads → Decision Layer checks
(invoice) and understands completeness, plausibility,
tax classification
│
┌────────────┴────────────┐
│ │
High confidence Low confidence
Rule clear or exception
│ │
Posting proposal Escalation to
+ audit trail specialistThe Document Agent does not replace specialists. It processes routine cases autonomously and escalates exceptions to humans -- with complete documentation.
2. Workflow Agents
Workflow Agents orchestrate processes across systems. When a document must be read, a decision made, and an action triggered in a target system -- the Workflow Agent coordinates the entire flow.
Example workflow: Sick note
Incoming → Document Agent → Decision Layer
(email with reads sick note checks entitlement
attachment) and extracts and WA constraints
data │
┌────────────┴────────────┐
│ │
Compliant Query needed
│ │
Calculate HR specialist
continued pay is notified
│ │
Propose SAP Waits for
booking decision
│ │
Audit trail Audit trail
documented documentedEvery step is logged. Every decision is traceable. On queries or missing information, the workflow pauses -- it does not abort.
3. Knowledge Agents
Knowledge Agents deliver context-based answers from enterprise knowledge. Works agreements, policies, collective agreements, compliance rules, FAQ catalogs.
Important: Every answer includes its source and rule version. A Knowledge Agent does not answer a question without citing a source. With missing or contradictory rules, it does not answer but refers to the responsible department.
Example: Question: "How many days of special leave do I get for moving?"
Answer: "According to Works Agreement WA-2024-007, Section 3 Para. 2 (Version 2024.2, valid since 01.04.2024), you are entitled to 2 working days of special leave for moving. For employees covered by the collective agreement, Section 29 TV-L additionally provides 1 further day."
Source included. Rule version documented. In case of interpretive ambiguity: referral to HR.
Decision Layer
The Decision Layer is the central governance component. It sits between agent and target system, making every LLM decision transparent, auditable, and traceable.
What it checks: professional rule sets, model confidence score, decision risk score, works agreement constraints, bias and discrimination potential.
What it produces: complete audit trail entry per decision, input hash for reproducibility, rule version per applied rule, routing decision (autonomous or Human-in-the-Loop).
Integration
AI agents do not replace existing systems. SAP remains ERP. Workday remains HCM. DATEV remains tax system. Agent logic is decoupled from the target system.
- SAP FI/CO, SAP S/4HANA
- SAP SuccessFactors
- Workday
- DATEV
- SharePoint, Microsoft Teams (via Microsoft Graph)
- Additional systems via REST/SOAP interfaces
Model Agnosticism
The architecture is not tied to a single LLM. The Model Layer is interchangeable:
- Claude (Anthropic) -- currently strongest model for complex text analysis
- ChatGPT (OpenAI) -- broad range of applications
- Gemini (Google) -- deep integration with Google services
- Llama (Meta) -- open source, self-hosted possible
- Mistral -- open source, EU-based
- DeepSeek -- open source, cost-efficient
- gpt-oss (OpenAI) -- open source, self-hosted possible
When a new model becomes available, it can be integrated without changing the business logic. No vendor lock-in to a single model.
Our engineers are certified for Azure AI and cloud-native architectures. We deploy agents on Azure, GCP or your own infrastructure – model-agnostic and platform-open.
Business Impact
- Routine cases processed autonomously -- with complete documentation
- Exceptions escalated to humans -- with context and recommendation
- Consistent rule interpretation across all locations
- Every decision traceable for auditors and works council
- Scalable without proportional headcount increase
- Knowledge stays in the system -- not in individual people
Complete source code, all prompts, all rule sets belong to the client. After 12--18 months, you operate your agents independently. Gosign builds and enables -- then steps back.
Frequently Asked Questions about AI Agents
What data leaves the company?
None. Agents run in your infrastructure -- cloud, self-hosted, or hybrid. With cloud LLMs, the respective provider's DPAs apply. With self-hosted models, no data leaves your network.
How long does a pilot project take?
4--6 weeks to a productive PoC. Discover (1 week), Build (3--4 weeks). The first agent runs live in your infrastructure with Decision Layer and audit trail.
Is this works-council-compliant?
Yes. Human-in-the-Loop as architectural principle, complete logging, role concept, audit trail. Works agreements are mapped as explicit constraints in the Decision Layer.
Which models are used?
The architecture is model-agnostic. Currently: Claude, ChatGPT, Gemini, Llama, Mistral, DeepSeek, gpt-oss. Models are interchangeable without changing the business logic.