Agent Orchestration

A2A agent routing is the runtime decision of who should execute a task after the handshake: keep it with the incumbent (SELF), keep the incumbent as owner and recruit peers for uncovered requirements (COLLABORATE), or transfer ownership entirely (HANDOFF). The A2A protocol standardises discovery and transport; a router supplies the missing policy layer. That is the short answer. The rest of this guide is the part that actually decides whether the router helps or hurts: what a state-aware utility function prices, why the honest benchmark signal is wasted work rather than the utility column, and the mode mix behind those numbers — SELF rising from 20% to 42% of decisions while HANDOFF falls from 66% to 44% — which shows that checkpoint awareness mainly teaches a router to leave a task where it is. ...

Working with AI coding agents feels like leadership because it is. The unit of work changed from writing code to decomposing it, delegating it, and verifying the result. That shift is measurable: a 2026 NBER working paper found that a leader’s performance managing AI agents correlates at rho = 0.81 with their performance managing human teams — and still 0.69 after controlling for hard technical skills. That single finding reframes everything else in this guide. If managing AI coding agents were a disguised coding skill, the correlation with human-team leadership would collapse once you controlled for task-specific ability. It does not. What the AI task is actually measuring is the soft, transferable part of leadership: how you set expectations, calibrate trust, and run a review loop. ...

Letting agents do agent work means moving from prompting a single model to orchestrating a team of specialized AI workers that collaborate toward one outcome. The real bottleneck in 2026 is not model capability — it is coordination. In this guide, you will learn how to design agent teams, choose the right collaboration patterns, structure handoffs, and govern human-on-the-loop workflows that actually ship. What Does It Mean to Let Agents Do Agent Work? For years, “automation with AI” meant one model, one prompt, one answer. Andrew Ng’s research reframed this: a relatively weak GPT-3.5 model driven by an iterative agentic workflow scored 95.1% on HumanEval, while GPT-4 zero-shot scored only 67%. The lesson is not that bigger models are obsolete — it is that how you structure the work often matters more than raw model size. ...

A planner-executor agent workflow MCP orchestrator splits a hard task into two roles: a planner that decomposes the goal into a step-by-step plan, and one or more executors that carry out each step with tool access, all connected through Model Context Protocol (MCP) tool servers. This pattern handles open-ended, path-dependent work that a linear pipeline cannot, because the planner can re-plan mid-run based on what executors return. It is the canonical multi-agent orchestration architecture for tasks where the full sequence of steps is not known in advance. ...

The DeepSeek Harness Agent Workflow plugin (dsh-plugin-agent-workflow) is a standalone, installable Web UI plugin that adds a dedicated “Workflow” tab to DeepSeek Harness, letting you orchestrate and inspect multi-step agent runs as a clear execution chain. It renders your real recorded Session events — model requests, model responses, and tool calls — in chronological order, split by user conversation turn, with token and cache statistics that reveal exactly how context is reused across steps. Because it is read-only and non-invasive, it never adds messages, prompts, or tools to your requests, making it a trustworthy observability layer for anyone running complex, multi-step agent workflows on DeepSeek Harness. ...

I’ve been running production AI agent systems for over a year now, and the single biggest shift I’ve seen in 2026 is this: the best agents don’t work alone. The teams getting real leverage out of AI aren’t the ones with one super-agent — they’re the ones running five, ten, or twenty specialized agents that talk to each other. This isn’t a prediction. It’s already happening. Meta’s HyperAgents paper (arXiv:2603.19461) proved that multi-agent systems can solve problems no single agent can touch. A production field study from Calx showed six agents building 82,000 lines of code in 20 days for $250. And the infrastructure to make this work — protocols, SDKs, open-source orchestrators — is already here, just not widely adopted yet. ...