The author proposes an AI agent architecture inspired by human brain processes, using belief updates and decision loops, and seeks feedback from the AI community on its practicality and improvements.
I am experimenting with an agent architecture that is less “give the model a big prompt and trust its reasoning” and more like a controlled belief-and-decision loop. Not claiming it literally mimics the human brain. More that it borrows a useful pattern: maintain competing explanations, update beliefs from evidence, decide what to check next, then act based on consequences. Very simple example: a smart-fridge agent gets a “weird smell” signal. Possible worlds: someone spilled mango juice an egg is rotting fridge power failed and food is warming some other cause we did not model It starts with priors based on context: recent door-open events, temperature history, what food is inside, past failures, etc. Then it gets evidence. Say the temperature sensor reads 14°C. Instead of the LLM narrating “this seems concerning,” the system asks: How likely is 14°C under each world? Update prior → posterior using those likelihoods. How much uncertainty actually reduced? Entropy before vs. after. Which allowed question has the highest expected information gain next? For example, “is the compressor drawing power?” is probably much more useful than “what color is the fridge magnet?” Is that question worth its cost, latency, privacy impact, and reliability? Given the posterior plus action costs, should it notify the user, wait, run another check, or escalate to a human? The LLM can help extract signals, propose candidate hypotheses, and call tools, but it should not be the final authority over belief updates or actions. The controller owns the world list, priors, likelihood estimates, policy thresholds, logs, and escalation rules. Important parts I want to keep explicit: an “other / unknown world” bucket, so the system does not act like its hypothesis list is complete calibrated probabilities and provenance for priors/likelihoods expected value of information, not just entropy reduction a human escalation path when uncertainty remains high, the case is out-of-distribution, or the downside is asymmetric a trace showing whether failure came from missing worlds, stale priors, bad likelihoods, a bad question policy, or bad action costs The rough loop is: input → possible worlds → prior → evidence likelihoods → posterior → uncertainty / expected information gain → cost-aware action → human escalation if needed → outcome + calibration update Math/AI people: is this a sensible practical architecture, or am I reinventing POMDPs, active inference, Bayesian decision networks, belief-state planning, etc. badly? What would you change first to make this real and evaluable? Especially interested in: handling open-world hypotheses, learning/calibrating likelihoods without pretending the numbers are objective, separating “most informative question” from “question that most improves the actual decision.”
A technical analysis of two approaches to building self-evolving AI agents: model-based (via architecture like SSMs or transformer with fast-weight updates, and training methods) and harness-based (via memory or meta harness that can rewrite itself). The author provides practical recommendations for different audiences.
The author proposes a mental model where AI agents should maintain a separate memory layer (brain) that stores reusable understanding, distinct from their knowledge base (library), to avoid rediscovering the same information repeatedly.
The author builds two multi-agent AI systems with opposite design philosophies: ChaoticAI (collaborative, org-chart-based) and S.A.G.E. with RAAC (adversarial argumentation). The post shares reflections on memory architecture and the potential synthesis of both approaches.
A comprehensive guide on building reliable AI agents, explaining core components of perception, decision logic, and action interface, with insights from a former Meta engineer.