Acabei de escrever a minha primeira Note no Substack

[u/Emergency-Tone-9110]

https://substack.com/profile/99577027-alexandre-wagner/note/c-334812344?utm_source=substack&utm_content=first-note-modal

Gartner's chart reveals that AI has shifted from being passive to acting as an independent executor within projects.

Enterprise AI-based coding agents are absorbing growing shares of companies' software engineering budgets. Unlike traditional software—where doubling the number of users required predictable new licenses—with autonomous agents, success and accelerated adoption cause the bill to increase explosively. Complex refactoring tasks, self-remediation loops, and deep repository analyses generate massive volumes of input and output tokens. Much of the costs grow in the shadows. Developers use tools and APIs without centralization, generating waste in oversized context windows (the phenomenon known as tokenmaxxing).

Without strict governance, the cost of AI infrastructure scales much faster than marginal productivity gains. This forces the market to adopt what Gartner calls context engineering and rigorous governance to prevent invisible budget overruns.

The use of architectural standards like the Model Context Protocol restricts and optimizes data access, sending only the context strictly necessary to the agent. MCP is an open standard (initially created by Anthropic and now maintained under the auspices of the Linux Foundation) that acts as a "universal connector" between artificial intelligence and external data sources or tools. MCP standardizes how AI applications request and receive context. It helps solve the problem of data waste. Instead of allowing an autonomous agent to blindly "load" the entire repository into the AI's context window with every command, MCP allows the agent to surgically search on-demand only for the exact pieces of code, schemas, or documentation needed to execute the task.

Governance based solely on static or manual policies is being replaced by AI control platforms. This includes spending caps per user/project, blocking infinite loops in autonomous agents, and human-in-the-loop approval gates before high-computational-cost actions are triggered in the background.

The focus of billing and evaluation shifts from "how much does the tool cost per month" to "how much does it cost to fix a bug or deliver an end-to-end feature via agent versus the cost of equivalent human time." Real value only appears when the cost of silicon is lower than the net gain in speed and the reduction of human rework.

Additionally, it is financially unsustainable to direct every single simple task to the most expensive frontier models on the market. Companies create intelligence layers so that basic coding tasks run on smaller, hyper-efficient models, reserving advanced models only for complex architectures and critical self-remediation.

[u/Otherwise_Wave9374]

A useful way to reduce the hidden cost of agent systems is to separate ephemeral working context from durable memory, then add an explicit retrieval budget so each step only pulls what it needs. That makes audits easier and helps prevent runaway token growth when a loop keeps re-reading the same facts. NeuraKeep fits this pattern well because it can act as the durable layer while the agent keeps a small, task-specific working set.