Technique
Agentic.
Software that decides its own next step and acts through tools, which makes the permission boundary and the audit trail part of the design.
21 papers · Technique
Memory leaves the model
Two papers published four days apart in September 2026 reach the same structural conclusion from opposite ends of the stack. Jev-Mem takes the language model off the critical path of memory operations and reports a 6.6 times faster build, 36.7 per cent lower query latency, and accuracy that went up rather than down. Memory as Middleware argues memory should not live inside an agent at all. When an efficiency argument and a governance argument independently recommend the same boundary, the boundary is usually real.
Read the paper →- October 2026EngineeringAGA-48
DeepSeek-V4.1-Flash and the 890-byte KV cache
DeepSeek brings the global KV cache to 890 bytes per token, a quarter of DeepSeek-V4-Flash, by serving forty transformer layers from four shared caches and storing them in FP4. A reading of the actual mechanism: the three CSA2 modes, the Causal Encoder-Decoder that halves prefill, and the bounded replay that is really a cache retention policy. We reconstruct the 890 bytes from the published configuration, and argue the paper's quality costs are asserted rather than measured.
Luis Hasanaj · Read → - October 2026EngineeringAGA-40
OpenShell puts the enforcement outside the agent
NVIDIA has open-sourced a runtime for AI agents whose central claim is about location rather than capability: enforcement happens outside the agent's process, where it cannot be prompted away. A reading of the four parts, the formal policy prover, the credential that never enters the workload, and the point at which the open source runtime becomes a reference design for hardware NVIDIA sells.
Luis Hasanaj · Read → - October 2026EngineeringAGA-37
Gemini 4 Argon: the output limit is the story
Google DeepMind's Gemini 4 Argon raises the output token ceiling from 64,000 to 1,000,000, and that single number changes more about how agent systems are built than any benchmark in the announcement. A reading of what the new ceiling costs, why shipping a model without cyber guardrails to a named cohort makes capability an identity problem, and the one optimisation DeepMind deliberately refused to take.
Luis Hasanaj · Read → - September 2026OperationsAGA-33
WhatsApp is already your customer record
For a great many small businesses the real history of every customer relationship sits in WhatsApp threads on individual phones. It is the most complete record in the company and the least owned by it. What it takes to bring that into a system without breaking consent, and why the first thing to fix is not automation.
Luis Hasanaj · Read → - September 2026EngineeringAGA-30
OpenAI's dots: the boundary is the product
OpenAI launched always-on personal agents at DevDay, each running on its own cloud computer with a browser and access to more than 4,000 apps. The marketing is autonomy. The engineering is a carefully drawn permission boundary, and the single most important decision in it is that an agent left running while you are away can only read.
Luis Hasanaj · Read → - September 2026EngineeringAGA-35
Hardware-native AI: the alarm that sees
A position paper. The next operating system layer will be built into devices rather than shipped as an app on top of them, and home security is the clearest worked example: an alarm today is a deterrent that detects motion, while a camera that understands what it is looking at is a different category of thing. The interesting engineering is not the vision model. It is deciding how far up the response ladder a device may go without a person.
Luis Hasanaj · Read → - September 2026ResearchAGA-36
A chain of events beats one big model
A position paper. An operating system is not a conversation, it is a chain of recurring events, each one narrow and shaped the same way every time. Give each event deterministic control flow, its own memory and a small tuned model, and the arrangement beats a general language model orchestrating everything, on latency, cost and predictability. The honest limit is what a fleet of specialists costs to operate.
Luis Hasanaj · Read → - September 2026OperationsAGA-32
What an AI receptionist actually fixes
Most of the market sells this as never miss a call again. The honest version is narrower and more useful: it fixes the calls you were never going to answer, it does not fix the calls where the conversation is the product, and the deciding question is which of those you mostly get. A buyer's guide from a company that builds one.
Luis Hasanaj · Read → - September 2026OperationsAGA-34
AI implementation is an architecture decision
Most AI projects that fail do not fail on the model. They fail because nobody wrote down the number that should move, and because a slow expensive model was put somewhere a person was waiting. The three axes that actually decide an implementation are cost per completed task, speed to a finished outcome, and latency, and only one of them appears on a price list.
Luis Hasanaj · Read → - September 2026ResearchAGA-29
When the verifier moves inside the model
Four papers from one week in robotics, image generation and view synthesis all pay for an expensive correctness check during training and then delete it before inference. It is a real efficiency result and it removes the thing you would otherwise have logged. Reading DexAgent, InternW0-Delta, UMM-Reflection and GeoVerse together, and what disappears along with the verifier.
Luis Hasanaj · Read → - September 2026ResearchAGA-27
CLM-8B, Jev, and language model shape
In one week, four independent groups published work that stops treating the language model as a fixed-shape black box: a model that ranks instead of generating, one whose output is constrained to a single number, one that varies depth per token, and one that changes the generation order. Reading them together, and the engineering bill nobody is itemising.
Luis Hasanaj · Read → - September 2026EngineeringAGA-26
TypeSafe's System One: code orchestrates
A new model class argues that the control flow belongs in your code and the model should be a typed function call returning a calibrated probability. It is the strongest published case yet that the deterministic core is the cheap, fast, reliable part, and that putting a language model in charge of the loop is a transitional architecture rather than the destination.
Luis Hasanaj · Read → - September 2026EngineeringAGA-24
GPT-6 Astra's system card is a build spec
Read as safety documentation it is reassuring. Read as an engineering requirement it is a list of things your infrastructure now has to do: a halt state you do not control, an approval gate a machine must not be able to satisfy, credentials the model will find, and oversight that no longer rests on reading the model's reasoning.
Luis Hasanaj · Read → - September 2026ResearchAGA-23
Why the brain keeps two memory systems
Mammals evolved a fast episodic store and a slow semantic one because collapsing them destroys knowledge. A 2026 result shows AI agents rediscovering that the hard way: consolidating memories from ground truth solutions made a model fail 54 percent of problems it had already solved. The theory that predicts it, and what it means for building agent memory.
Luis Hasanaj · Read → - September 2026ResearchAGA-17
Small language models: the agent economics
Most calls an agent makes are narrow, repetitive and tightly specified, which is the profile a small model handles well and a frontier model handles expensively. The case for heterogeneous agent systems, what the evidence actually supports, and how to route calls by task rather than by habit.
Luis Hasanaj · Read → - September 2026EngineeringAGA-15
AI agent security: the real attack surface
An agent with credentials, tools and memory is not a chatbot with a bigger prompt, it is software that acts. This is the attack surface that creates, read against the OWASP Top 10 for Agentic Applications 2026, two live CVEs in agent infrastructure, and the first reported cyber espionage campaign run mostly by hijacked agents.
Luis Hasanaj · Read → - September 2026EngineeringAGA-16
MCP, A2A, ACP, AP2: the agent protocol stack
Four protocols keep getting written about as rivals. They are layers. MCP gives an agent tools, A2A lets agents talk to each other, AP2 proves a human authorised a payment, and ACP completes the purchase. What each one actually does, who governs it now, and what a business has to implement to be sellable to an agent.
Luis Hasanaj · Read → - August 2026EngineeringAGA-10
Engineering for the EU AI Act: Article 50 transparency as system architecture
How we build the EU AI Act's Article 50 transparency duties into AI-native products as architecture rather than configuration: disclosure at first contact, provenance marking at generation, recorded human review and per-message jurisdiction, read against the timeline as amended by the 2026 Digital Omnibus.
Luis Hasanaj · Read → - March 2026EngineeringAGA-03
Agentic commerce: a reference architecture for AI checkout under merchant control
A reference architecture for letting AI agents discover products, apply promotions, and complete secure checkouts on a customer's behalf, over a standardized agent-to-merchant protocol layer that keeps pricing, payments, and compliance under merchant authority.
Luis Hasanaj · Read → - February 2026ResearchAGA-02
Governed autonomy: approval lanes and audit trails in agentic systems
How to give AI agents real autonomy without losing control: a pattern of approval lanes, reversible actions and complete audit trails that lets a business trust software to run part of its operation.
Luis Hasanaj · Read →