`ExternalCodingRunner` is Looplane's second runtime lane. The external coding CLI owns its model loop and credentials; Looplane hands off a task and disposable clone, then treats the returned patch as untrusted input and reruns path audit, verification, and the source invariant. This is a capability-bounded handoff, not another `ModelProvider`.
Dynamic workflows let Claude write multi-agent orchestration as a JavaScript script that a runtime executes in the background — up to 1,000 agents per run, savable as a /<name> command. This piece covers trigger methods, the save-and-rerun flow, three fit scenarios (codebase audit, large migration, cross-checked research), and where they differ from Agent Teams.
AG2 continues AutoGen's ConversableAgent model: agents collaborate through messages, while GroupChatManager selects the next speaker by round robin, manual choice, randomness, or an LLM.
These seven tools are not one product category: LangGraph, MAF, and Mastra emphasize durable workflows; CrewAI and AG2 emphasize multi-agent collaboration; Pydantic AI emphasizes typed Python agents; DSPy optimizes AI programs against data and metrics. Choose the control model first.
Kubernetes is fundamentally API objects, controllers, and reconciliation loops—not YAML; it manages workload lifecycles without solving application state, data consistency, or organizational governance.
Microsoft AI-500, AB-620, AB-100, NVIDIA NCP-AAI, and Claude CCAR-F all test multi-agent architecture, and they overlap on seven things: orchestration topologies, A2A and MCP, per-agent identity boundaries, three-layer memory, observability and agent replay, human-in-the-loop, and guardrails at four intervention points. But four vendors use four vocabularies for the same ideas, and each exam has objectives that don't transfer — Microsoft names four context-window failure modes nobody else names, 7% of NVIDIA's is locked to NeMo and NIM, and Claude tests SDK-level details like stop_reason. One correction along the way: Google PMLE's wall-to-wall 'Agent Platform' is a Vertex AI rename, not a multi-agent domain.
How an individual connects tools is a preference; how an organization does it is governance — who can touch what data, where keys live, whose budget it lands on. Anthropic's published record of ten internal teams contains a good indicator: security engineering accounts for 50% of all custom slash commands in the entire monorepo. Adoption doesn't spread evenly; it takes off first in teams that already build their own tools.
Background agents replace 'you watch it run' with 'it finishes and opens a PR.' Every vendor's design converges on the same parts: an isolated environment, external triggers (issues, Slack, Linear), and a PR as the output. The genuinely new problem is that you become the bottleneck — five agents finish at once, five diffs queue for you, and none of them know the others exist.
The final session is 'self-running, self-improving software systems.' The parts all appeared in the previous nine weeks: deterministic validation loops, skills that can be written back, background agents, centralized governance. One easily missed proportion from the slides — coding is 30% of engineering time, and running it in production is the other 70%.
Salesforce's number from 20,000 deployments: 90% of the work on an agent happens after launch, the reverse of traditional software. Stripe merges 1,300 PRs a week with no human-written code, and credits the environment rather than the model.
Microsoft, OpenAI, Salesforce, Stripe and three others independently say the same thing: reliability comes from the engineering around the model. And 'give the deterministic parts back to code' has been shipped as a product four separate times — Agent Script, Procedures, runtime, blueprints.
At 99% accuracy per step over 100 steps, the error-free completion rate drops to just 36% -- error compounding is a structural problem, not something prompt tuning can fix. Distributed systems' supervisor trees, bulkheads, circuit breakers, sagas, and durable execution can be mapped almost one-to-one into agent orchestration. But LLMs introduce a failure class that traditional systems never had -- semantic errors that don't crash -- which require Inspector agents (recovering 96.4%) and redundancy voting (MAKER: one million steps with zero errors) to address.
Claude Octopus is a Claude Code plugin that simultaneously calls Codex, Gemini, Copilot, Qwen, Ollama, Perplexity, OpenRouter, and Claude to review the same code, using a 75% consensus threshold to catch single-model blind spots. It ships with 32 personas, 48 /octo:* slash commands, 51 skills, and a Dark Factory fully autonomous spec-to-code pipeline.
oh-my-claudecode (OMC) adds 8 collaboration modes, 19 specialized agents, and cross-model orchestration (Claude + Codex + Gemini) on top of Claude Code, transforming a single-user CLI tool into a multi-agent development platform. Features include Deep Interview for requirement clarification, Smart Model Routing that saves 30-50% on tokens, and automatic rate limit recovery.
oh-my-openagent (OmO) transforms OpenCode from a single-LLM tool into a multi-model agent team — Opus as the workhorse, GPT-5.2 as the architect, Gemini for frontend, Sonnet for documentation lookup — all triggered to run in parallel with a single ultrawork keyword. With 48K stars, it is the earliest project in the UltraWorkers ecosystem to establish the multi-agent coding pattern.
Google outlined eight multi-agent design patterns: from the simplest Sequential Pipeline to the composable Composite Pattern. More complexity isn't always better — picking the right pattern matters more than stacking agents.
LangGraph models LLM workflows as directed graphs, solving the pain points of multi-turn iteration, conditional branching, and parallel execution that are difficult to handle with linear pipelines.
A single RAG Agent handling all queries hits knowledge boundaries and performance bottlenecks. Multi-Agent RAG dispatches retrieval tasks to multiple specialized Agents, each with its own knowledge base and retrieval strategy, coordinated by a central Orchestrator that merges results.