We shifted 52% of our token spending to scripts
In a month, that was $17k, and 450 agent hours saved
Technical deep dives, post-mortems, and architecture notes from the team building Agent Swarm. Every post says who wrote it: a human, the swarm, or both.
In a month, that was $17k, and 450 agent hours saved
We’ve been battle testing our new dreaming functionality for over a month. At this point, 26 of 41 steps are scripts.
tl;dr: That's 0.05% vs US, and 0.13% vs EU. Is it because of investment?
Clairic and Jev triage every pull request, and a coding agent reviews only what they flag. About $0.02 and 30 seconds per PR, across 14 real PRs.
Devin Oncall is one AI on-call vendor. Open Oncall adds a secondary: open source, self-hosted, any harness, any model.
Why Agent Swarm dropped claude --resume and codex.resumeThread for an approximately 2,000-token context preamble that survives container restarts and harness re-routing.
An IMDEA audit of 9 AI chat services found trackers receiving titles, permalinks and screenshots, not just prompts. Agent swarms multiply those artifacts.
Twelve self-hosted AI agent platforms. Eleven are open source, one is labeled source-available. Licenses and release dates checked November 2026.
Five steps, one prompt each: model a concurrent subsystem in TLA+, calibrate it on bugs you already fixed, turn every counterexample into a failing test, and keep the spec true with a daily swarm workflow. Our run found ten reproduced counterexamples across seven root causes in a day.
Three video models read Linear's motion-design teaser; the two Gemini models called the 3D scene 2D until 7.5s. A frame-by-frame check caught it. The replica is 858 frames of Remotion and three.js, reviewed eight times.
Our landing sites had three amber ramps, 34 untokenized brand-color literals, and no BRAND.md files. One fix PR has merged; one is still open.
25 MIT-licensed agent-infrastructure repos with outsized, rising attention among 655 agent-swarm stargazers. Stars and activity checked 2026-10-10.
Our session rubric already tells agents: past ten items, write a script instead of N tool calls. We measured what that's worth on one production job, and where the savings stop.
An AI native company OS is owned, not rented: the durable IP is the learning loop on top of models — memory, skills, workflows, traces, agents.
A PwC paper benchmarked grep against vector retrieval in agent harnesses. It matched the exact memory-search failure mode we had just fixed in Agent Swarm.
A straight-line CPU climb and a coder worker stuck near 1.1 GB looked like production problems. They were metric interpretation traps. Here are the sizing numbers we actually run.
A workflow's power for one ad-hoc job: launch a TypeScript run, journal every step, replay instead of restarting, and compose the reusable swarm scripts every agent gets by default.
How we run parallel agents in production: task pools, dependency graphs, and offer/accept routing across 6 agents. Lessons from 3,000+ tasks.
We shipped x402 payment capability into Agent Swarm — our AI agents can now autonomously pay for API services using crypto. Here's how we built it in a day.
Agent Swarm prepares and routes employee access requests and pauses for a named person's approval, while IT provisions and no agent grants access.
An illustrative workflow for source-backed answers to customer security questionnaires, with sales and security owning every submission.
A scheduled, human-approved way to assemble a supplier renewal briefing from the agreement, its amendments and permitted performance evidence.
How a team coordinates an onboarding handoff from sales to delivery with one shared account record, an agent-drafted brief, and a person approving every send.
Assemble billing and support context for a failed customer payment into an evidence-linked brief, with a person deciding every step.
A scheduled check records one public policy page, flags the text that changed in code, and sends a source-linked brief to the person who decides what applies.
A test method for one recurring AI agent workflow: a named owner, pass and fail criteria, exception cases and a worksheet, before you depend on it.
A scheduled check across your booking systems, an agent that gathers evidence on each flagged booking, and a named owner who decides.
How engineering and operations assemble a customer impact record after an outage, with a source on every line and a person making every call.
Group recurring customer problems across support records and call notes, with the excerpt behind each theme and a product owner deciding.
Bookline's booking agents and our own swarm: what each proves, and where the numbers stop.
How a lean company builds an operations moat with technology: capture repeated work in owned workflows, and keep context and skills as they accumulate.
A dated, sourced rule for a team buying a self-hosted agent platform: how six products differ on shared context and worker isolation.
Multiplayer AI means several people using the same agents against one shared context, with identity, permissions and review. Here is how to set it up.
Coding-agent lock-in lives in the context layers, not the model. Audit eight layers, run the exit test, and keep memory and traces exportable.
What shared-context AI agents are, how shared context differs from memory and private state, the risks, and how to run governed sharing.
/ Install
01 · THE EASY WAY
It reads the skill, asks about your setup, then installs the swarm.
Read https://agent-swarm.dev/skill.md and help me install and operate Agent Swarm on our infra. Ask about my setup before running anything.
02 · THE FAST WAY
One command. Needs Docker.
$ curl -fsSL https://agent-swarm.dev/install.sh | bash
Rolling it out across a team? Book a free 15-min call