Agentic AI: planning, tools and accountable execution
A research and engineering guide to bounded agents, approval gates and measurable task outcomes.
Prepared on 20 Oct 2026 for editorial review. Product specifications and company research results are not asserted unless verified.
Define the task boundary
Start with a narrow goal, an explicit completion condition and a list of permitted tools. A document lookup assistant and an agent that initiates a payment have different risk profiles. State what the agent cannot do and how it hands incomplete work back to a person.
Keep orchestration state outside the language model: approved actions, remaining budget, tool results and review status should be recorded by the application. A model-generated plan is a proposal, not an authorisation.
Separate reasoning from authority
Tool access must enforce the signed-in user’s permissions and validate every argument. Use restricted network destinations and minimal data fields. Require a human approval gate before consequential actions such as financial instructions, external communications or record deletion.
Treat tool output and retrieved documents as untrusted evidence. Do not let instructions embedded in those sources change the agent’s permissions or expose secrets.
Evaluate complete tasks
Build an independent suite covering success, missing evidence, tool failure and malicious content. Measure task completion, unsafe action attempts, escalation, latency and cost, with a reproducible record of model and tool versions.
Begin in a sandbox with simulated side effects. Expand only after the failure cases and recovery path have been reviewed; this guide does not claim a deployed KaribuTech agent or completed benchmark.