Knowledge / Agentic systems
Bounded autonomy to act within clear limits.
An agentic system plans and uses tools to achieve a goal under policies, oversight and stop conditions.
- 01Goal and context
Define the task, scope and limits.
- 02Plan and orchestration
Separate steps, budget and exit conditions.
- 03Agent, tools and skills
Use authorized resources in bounded flows.
- 04Evaluation and review
Check rules, evidence and human approval where applicable.
- 05Result or escalation
Record the output and stop or escalate according to policy.
Plain definition
What it is and what problem it solves.
An agent combines goal, state, tools, planning and feedback. It is not the same as a chatbot or a linear automation.
It is useful when a task requires observing context, deciding the next step and operating authorized tools, without promising full autonomy.
Architecture and limits
Technical design with explicit decisions.
Reference architecture
Autonomy must be designed around a concrete flow.
- Explicit goal, state, tools, plan and feedback.
- Single-agent for bounded flows; multi-agent only when responsibilities justify it.
- RAG or Graph RAG for context, not as a substitute for control.
Limits and oversight
Every action requires a clear policy.
- Permissions per tool, data and action.
- Stop conditions, human escalation and reversibility.
- Traceability of plan, tool calls and results.
Risks to address
The design must anticipate operational failures.
- Ambiguous goals, tools with unwanted effects and loops.
- Evaluation with real scenarios before expanding autonomy.
- Continuous observability and policy adjustments, not invisible patches.
Loops and agents
Agents operate within defined sequences.
A useful agent does not replace the organization’s judgment: it speeds up tasks and decisions within a designed, measurable and supervisable loop.
Critical flows
We prioritize sequences where querying, analyzing or acting has a concrete operational result.
Connected tools
Agents use authorized APIs and systems to query or run bounded actions.
Continuous verification
Results are checked against rules, evidence and human review when appropriate.
Expected outcome
Capability that can be explained, operated and measured.
- Less repetitive work without losing control.
- Processes with explicit steps, limits and metrics.
- Learning based on operational results.
Relationships
The system works as part of a platform.
Let’s turn knowledge into an operable capability.
We define the case, the sources, the limits and the evidence needed before building.