Webinar: AI agents, tools, and safeguards explained

APA is the software category Kognitos defines: agents that own multi-step enterprise processes end-to-end, interpret unstructured inputs, enforce written policy deterministically, raise exceptions in plain English, and learn from human resolutions. RPA handles structured, rule-based tasks but breaks on exceptions; copilots help individuals draft and summarise. APA replaces the process, not just the keystroke or the email draft.

Automation and AI programme owners, CIOs, RPA centres of excellence ready to move past bots, solution architects evaluating agentic AI for enterprise rollout, and developers and business analysts who want to see how an agent is authored and operated. The technical deep-dive segment is especially relevant for engineering and platform teams.

The Kognitos brain is a neurosymbolic runtime: an LLM interprets language and unstructured inputs while a symbolic execution layer enforces policy deterministically. Knowledge lives in 'books' (collections of micro-skills with credentials, test and production environments), an architect agent plans execution given an SOP plus available capabilities, and a human-in-the-loop pattern handles ambiguity. The whole platform is designed so the unpredictable parts of AI are contained inside a predictable shell.

Because the consequences of being wrong on a customer-facing decision compound: 99.9% accuracy across 100 steps still produces meaningful error volumes, and even single-step 99.9% is unacceptable when the affected record is a payment, a claim or a patient. Kognitos's design point is 100% governed accuracy: deterministic policy on every decision, with the LLM constrained to language interpretation only.

The architect plans an execution path using your SOP, your books and available micro-skills, and produces a candidate plan. The plan is tested in a staging environment, results are presented side-by-side with the previous baseline, and a human decides when (and how much) to push to production. The runtime captures every decision, so any post-deployment surprise is replayable and explainable.

The goal is roughly 90% of work done autonomously and 10% routed to a human for the genuinely ambiguous cases. When a human resolves an exception they answer in English; that answer becomes part of the policy so the same exception is handled autonomously next time. The autonomous share grows quarter over quarter without anyone retraining a model.

An audience question prompts this answer. Kognitos uses long-term memory inspired by human memory, but with one critical difference: where humans are about 99.9% accurate on remembered facts, an enterprise agent needs to be 100% accurate. Memory is stored, retrievable and replayable with full lineage, so a decision made by the agent today can be defended against the exact context that was in memory when it was made.

Three paths are offered in the closing. If you have an active use case and want a working demo against your data, request a personalised demo at sales@kognitos.com. If you want to try the platform yourself, join the Kognitos community edition (kognitos.com/community) and use the pre-built agents and the Auto-Write feature. If you are a developer or RPA practitioner, the documentation and Academy give you a self-serve path to your first production workflow.

Yes. The session was recorded live; every registrant receives a recording within 24 hours and it is available on demand on this page. The slides and follow-up resources are shared by email, and the Kognitos team is happy to schedule a deep-dive with your platform or automation engineering team to extend any of the technical sections.