Buying guides
Agent tools in 2026: sort them into three layers before you buy
Every founder shopping for an agent framework is really shopping for the same thing: the tool that finally lets them stop hiring. It is a reasonable hope, and it is aimed at the wrong target.
The teams actually compounding value from agents report the opposite of what the pitch promises. Dan Shipper, who runs Every as an early-adopter lab, put it plainly in "After Automation" (May 2026): the more they automate, the more expert human work there is to do. Their work looks nothing like it did a year ago, but the humans did not leave. They moved.
Hold onto that before you compare a single tool, because it explains why the comparison you are about to run is the wrong one.
The map: three layers, and most comparisons cross them
Almost every "X vs Y vs Z agent" post ranks tools that live on different layers of the same stack. There are three.
Brain. The model that reasons: Claude, GPT, Gemini, open-weight releases. This is a commodity, and the price is falling roughly an order of magnitude a year. Whatever you standardise on today is matched by a cheaper release within months.
Hands. The runtime that gives the brain a body: a terminal, a browser, files, tools, memory. This is where the tools people argue about live, and it is commoditising fast and in the open. By mid-2026 the open-source runtimes had reached a scale that was unthinkable a year earlier: OpenClaw past 380,000 GitHub stars (MIT), Nous Research's Hermes Agent past 210,000 (MIT), Block's goose past 50,000 and now donated to the Linux Foundation's Agentic AI Foundation (Apache-2.0).
Manager. The layer that coordinates many agents: roles, budgets, delegation, audit. The open example everyone points at is Paperclip (past 70,000 stars). For almost everyone it is premature: coordination only has value once you are already running several agents worth coordinating.
Ranking a hands tool against a manager tool is ranking a hand against a supervisor and asking which grips better. Sort any tool into its layer first. Then the only useful questions are the per-layer ones.
The two tools you asked about
Omnigent is the clearest signal of where the hands layer is going. Databricks open-sourced it in June 2026 under Apache-2.0, led by CTO Matei Zaharia (the creator of Apache Spark and MLflow). It is a meta-harness: it orchestrates Claude Code, Codex, Cursor and others behind one interface, lets you swap the underlying harness without a rewrite, and enforces policies, spend caps and sandboxing at the platform layer. It reached the high thousands of stars within about a month.
Read that sentence again as a buyer. Databricks is giving away, for free, under a permissive licence, the swap-any-harness, cap-the-spend, sandbox-and-approve machinery that harness vendors were trying to sell. That is not a tool to get excited about owning. It is proof that the hands layer is not where durable value lives. (It is genuinely useful, and it is four-week-old alpha with governance features most teams do not need yet: pilot it, do not build your company on it.)
SkillSpector is the more interesting one, because it points at the layer that does matter. It is NVIDIA's open-source (Apache-2.0) security scanner for agent "skills": it inspects a skill before you trust it, flagging prompt injection, data exfiltration, privilege escalation and supply-chain patterns, and emits SARIF so it drops straight into CI. It exists because skills turned out to be dangerous at scale. A January 2026 study of 42,447 agent skills pulled from the major marketplaces ("Agent Skills in the Wild", Liu et al.) found that 26 percent contained at least one security vulnerability and 5 percent showed signs of outright malicious intent.
That number is the tell. Skills are where the reusable value accumulates, which is exactly why they are worth attacking, and why they need a gate.
The layer that actually compounds
If the harness is a commodity and the models are a commodity, what is left to own? The loop that sits on top of them.
The pattern worth copying is the one Hermes Agent made legible and that every serious operator ends up rebuilding: run a task, write down what worked as a reusable skill, pull that skill next time, improve it, and let it compound over weeks. No retraining, no GPUs. It runs on ordinary API access, and it is prompt and skill editing, not weight training. It is a library of your judgment, encoded as skills the agents read.
The ecosystem has already standardised the container for this. The Agent Skills format (SKILL.md), which Anthropic originated and then opened under Apache-2.0, is now a cross-vendor standard that Claude Code, Codex, Gemini CLI, Copilot, Cursor, goose and others all read. That is what makes SkillSpector matter: when skills are a portable, shared asset, scanning them before you trust them stops being optional. The library is the thing with value. The harness just reads it.
Every agent needs a human at both ends
Here is the part the launch posts skip. The further an agent gets from a human responsible for it, the worse it works. Someone has to point it at the right thing, decide whether the output is good, catch where it is confidently wrong, and turn the result into a real decision. Shipper calls it the human "sandwich": the person is the bread on either end of the agent's work.
There is a deeper reason this does not go away with a better model, and it is worth understanding because it is the whole argument. Models are trained on the visible residue of finished human work, so they make yesterday's competence cheap and hand it to everyone at once. When everyone has the same cheap competence, the default output converges on sameness. Sameness is slop, and as Shipper defines it, slop is not a typo or an em dash: it is visible sameness, repeated until you can smell it. The moment competence is abundant, the scarce and valuable thing becomes what is different, specific, and right for this customer, this codebase, this moment. That is a demand for judgment, and judgment is where the human sits.
So automating a task does not delete the expert. It floods the world with plausible first drafts of that task and creates a new, larger job: deciding which of them is any good, and fixing the ones that are not.
"But the benchmarks are going straight up"
The standard objection is that this is temporary: the models will catch up. It is worth knowing why the benchmarks read scarier than the reality.
Benchmarks measure a model inside a frame. To score anything you first freeze the problem into a fixed prompt, and the score tells you how well the model does inside that frame, which someone chose. Shipper's team showed this concretely with a senior-engineer refactor benchmark: change the prompt and the same model's score swings from near zero to competitive. When a frame saturates, you change the frame and the number collapses again, and the work inside the old frame gets cheap enough that everyone attempts it, which manufactures more messy attempts for experts to judge. The edge keeps regenerating one level up. Progress is real; so is the gap.
You cannot operate what you cannot prove
An agent you cannot measure is a liability with good manners. If a human is accountable for the outcome, they need the outcome as a number: how often the agent is right, where it fails, what a step costs, whether last week's fix held.
This is the same discipline whether the thing being proved is code or work, and the tooling for it is fully open-source and maintained in 2026. For agent behaviour you have promptfoo for declarative CI checks with native tool-call and trajectory assertions (OpenAI announced it is acquiring promptfoo in March 2026, and it stays open source), DeepEval for pytest-style tool and task-completion metrics, the UK AI Security Institute's Inspect for rigorous agent-trajectory and multi-agent evaluation, and self-hosted Langfuse for production traces and online scoring (acquired by ClickHouse in January 2026). Two you should not build a business on: Arize Phoenix ships under the Elastic License, which is source-available and forbids offering it as a hosted service, and Braintrust is a proprietary competitor. Evals as a number, a human on the calls that matter, an evidence trail. The tool is swappable. The proof is not.
The only comparison that matters: operated versus do it yourself
Strip it all back and there are two honest choices, and neither is "which framework".
Do it yourself wins when you have an AI-native operator on staff who enjoys the maintenance, when the task is a one-shot, or when tinkering is the point. The tools are good enough that a capable engineer can stand up a working agent in an afternoon.
Operated wins when the work is production and ongoing, when a wrong or late answer is a real cost, and when nobody on the team wants to own the breaking updates, the spend caps, the permissions, the skill scanning, the evals and the human review queue. That is most of the mid-market, and it is exactly the layer the honest DIY writeups quietly tell you to outsource or wait a year for.
That is where we work. We operate at the hands layer on a proven, swappable harness, we own the learning-loop skill library on top of it, we scan and gate every skill, we cap the cost, we keep a human on the calls that matter, and we prove the behaviour with the same eval machinery we use to prove code. We do not sell you the harness. We sell you the outcome and stand behind it.
The tool you agonise over this quarter will be a footnote by the next. The operating discipline compounds the whole time. Pick the operation, not the tool.
If you want to see it on one workflow, our Discovery Pod turns your best candidate into a working, measured digital worker in two weeks, and everylayer is the same proof discipline pointed at the code your agents write.