feedback loops
5 stories filed under feedback loops on Beyond Market Intelligence. The newest of them: “From 40 Trillion Tokens, a Blueprint for Scaling AI Agent Trust”, “Move beyond basic RAG with four knowledge graph patterns for agentic AI.”, and “From code writer to boundary architect redefining software engineering”. Trust is the bottleneck, not model performance. Knowledge graphs are moving from the background to the backbone of agentic AI, and Cassie Shum is here to show you why. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every feedback loops story on Beyond Market Intelligence, newest first.
From 40 Trillion Tokens, a Blueprint for Scaling AI Agent Trust
Trust is the bottleneck, not model performance. Brendan O'Leary draws on 40 trillion tokens of usage data to map the four rungs of AI adoption, showing where context construction, multi-model routing, and feedback loops actually break down. For engineering leaders scaling agentic workflows, this is a practical blueprint. For deeper context on the infrastructure demands these agents create, see our companion piece on how CedarDB unifies operational and analytical data.
Move beyond basic RAG with four knowledge graph patterns for agentic AI.
Knowledge graphs are moving from the background to the backbone of agentic AI, and Cassie Shum is here to show you why. In her talk, she moves past basic retrieval to outline four practical architecture patterns that bring reasoning into production. It's a grounded, actionable look at building systems that don't just retrieve, but reason. For a broader perspective on how these ideas connect, our piece on bridging retrieval and action offers a useful parallel.
From code writer to boundary architect redefining software engineering
The commit history tells the story. Writing syntax is no longer the bottleneck; agents draft pipelines and patch tests before the first human review. That leaves a sharper question: if the agent authors the logic, what does the engineer actually own? The answer is the boundary itself. Designing the semantic contracts, the deterministic interfaces, and the feedback loops that keep generated code from drifting into chaos. That is the new equilibrium.
Agentic fitness functions bring nuanced judgment to evolutionary architecture.
Deterministic rules can guard hard metrics, but they go silent when architectural intent is on the line. That's where Hemant Kumar Mahato, Łukasz Sieczkowski, and Vijayasenthilkumar Kuppusamy turn our attention. Their piece on agentic fitness functions pairs AI agents with versioned rubrics to assess judgment-heavy concerns like boundary fidelity and stale ADR assumptions. It's a practical, forward-looking approach to governance. For readers still weighing AI's judgment, our earlier piece, "Talking to My AI Clone Taught Me to Question the Tech," offers a fitting counterpoint.
Rethinking AI Memory: From Descriptive Facts to Actionable Context
The essay's question about AI memory hits a real nerve. We're currently building systems that remember *what* you said, but we rarely ask them to learn *how* you think. Shifting from storing descriptive facts to inferring your reasoning patterns feels like the natural next step. It moves memory from a static notebook to a live model of your mind. Whether this evolves organically or demands new architecture is the core tension.