ABOUT
Repetitive work leaves your desk. The decision stays yours.
AgenticosCore designs and operates AI agents that reduce manual work, connect existing systems and retire applications that no longer justify their cost and complexity. Every engagement starts from the process, the rules and the risks — not the technology.
The honest boundary
- Autonomy is restricted to reversible, pre-authorized tasks. Production changes, privileged access, money movement and irreversible actions require explicit approval.
- Trail, Brake, House Rules and Parallel turn that limit into operational governance.
Operating model — control before scale
- The agent only moves forward when scope, data, integrations, exceptions and approvals are defined.
- Diagnostic — assumptions and risk before implementation.
- Parallel — validation before any cutover.
Why Core
- The core is not one isolated model. It is the control layer — Trail, Brake and House Rules — that lets execution be delegated without transferring accountability for critical decisions.
Operating commitments
- We do not treat agents as unrestricted substitutes for people or accountability.
- We do not project returns without operational assumptions and data.
- We do not move forward without proper access, auditable controls and a clear stop criterion.
Credentials — translated for your operation
- Automation and orchestration — for your operation: flows designed with state, exception handling and recovery.
- Systems and data integration — for your operation: connections evaluated by access, data quality, volume and criticality.
- Operational governance — for your operation: rules, approvals, trail and interruption criteria defined from the start.
- Continuous operation and evolution — for your operation: observability and performance review built into the delivery cycle.
Applied AI — open when it makes sense
- Local LLMs and open weights (Ollama · Qwen · Llama · self-hosted): keep sensitive context on your infrastructure when the case calls for it.
- RAG and sourced memory (RAG · VectorDB · citations · access control): connect the model to authorized knowledge, with visible origin and access scope.
- Agentic orchestration (n8n · MCP · Agent Hub · human-in-the-loop): connect data and tools with queue, state, approval and recovery.
- Multi-LLM routing (GPT · Claude · Gemini · Kimi · GLM): each step can go to the most suitable model — local or API — with explicit fallback, cost and criticality in the routing.
- Evals, observability and cost (evals · fallback · tokens · telemetry): structured output, tests, monitoring, prompt caching, budget and redacted logs before calling anything production.
- A model only becomes an operation when it crosses the whole flow: Data, Context, Model, Tool, Approval, Trail.
- “Open source” can mean open software or open-weight models. License, privacy, cost and quality are validated for each case before the choice.
No lock-in by default
- The model enters as a replaceable part; your data, your rules and your Trail remain yours.
How we run delivery
- Process Diagnostic — we map volume, rules, exceptions, systems and approvals before defining the solution.
- Controlled scope — we start from a verifiable boundary, with objective success and interruption criteria.
- Parallel operation — we compare the agent's outputs with the current process before any replacement.
- Evidence-based decision — the Trail records every action; continuation depends on the data observed in operation.
Start with the process that consumes the most time
- The Diagnostic organizes process, volume, systems involved, exceptions and approvals to determine whether an agent makes sense — and at which size.