Tolics Engineering Mind

A practical six-level AI maturity framework for companies adopting AI. Assess strategic clarity, data readiness, and engineering capability to find your gaps.

A-MEM's Zettelkasten-inspired approach to agent memory, and why software projects need federated memory with cross-domain linking — not one big vector database.

Fixed pipelines over-process simple requests and under-process complex ones. An adaptive router classifies intent and dispatches to specialised workflows.

AI-native is more than a buzzword. It marks a real architectural shift where AI becomes the foundation, not a feature bolted on.

Productivity culture destroys the spontaneous, unstructured time where creativity and connection actually happen. Become more human, not more efficient.

RAG pipelines leak PII through prompt injection. A substitution layer swaps real data for realistic fakes before it reaches the LLM, neutralising the attack.

Probabilistic lead scoring with Pyro replaces brittle point systems with honest uncertainty, email fatigue modelling, and self-improving predictions.

Elixir and OTP offer actors as agents, supervision trees for failure recovery, and process mailboxes as thought logs — a natural runtime for living software.

What would a programming language designed for AI look like? The thought experiment reveals deep unsolved problems and points to probabilistic programming.

Stability contracts, consequence graphs, immutable event logs, and a conductor that turns the gap between intent and reality into autonomous work.

Traditional observability counts individual leaves. In a living system, ambiguity is the vital sign — it reveals where the system is still becoming.

We build AI systems that mimic human workflows. AI-native software should look like a living organism, not a faster factory pipeline.