Multiagent teams
From single agents to multi-agent teams
By 2026, “we have an agent” will sound as dated as “we have an app.” The single-agent era – one model, one prompt, one linear workflow – will be mostly over. Enterprises will still use these Level 1 and Level 2 agents (tool use, basic chaining), but the real leverage will come from multi-agent teams: coordinated swarms of specialized agents that divide work, double-check one another, and repair failures without waiting for a human.
“The real jump isn’t one smarter agent; it’s multi-agent teams that chunk problems, specialize, and cross-check each other.” — John Cutter
The market will get there the hard way. We’re about to hit peak disillusionment: every vendor claiming “autonomous agents,” most of them just scripting a model to call APIs in a loop. Teams are already feeling the pain. Once you go beyond a couple of dozen agents, the infrastructure starts to creak. In 2026, that frustration will force a clearer hierarchy:
The shift from Level 2 to Level 3 is the real breakpoint. Instead of one over-burdened “do-everything” assistant, you will see teams of narrow specialists: one agent for data extraction, one for policy compliance, one for customer tone, one for optimization, one acting as a coordinator. They will escalate to humans the way a junior team would: with context, alternatives, and a recommended path, not a raw log of errors.
Crucially, these won’t be theatrical, personified “coworkers” with names and avatars. By 2026, the skeuomorphism phase – pretending every agent is a little digital employee – will be largely over. Agents will look more like problem-chunking machines operating in a mesh: continuously breaking work into smaller units, routing those units to the right specialist, and recombining the results into actions and updates across systems.
To get there, enterprises will have to solve three hard problems.
First, coordination and memory. Multi-agent systems need a shared state: what’s already been tried, what constraints apply, what “good” looks like in this domain. That will push teams toward explicit playbooks and reinforcement-learning environments where agents can practice on simulated workloads before touching production. You won’t trust a swarm of agents with your revenue cycle until they have survived thousands of dry runs.
Second, infrastructure. Running two agents is easy; running 200 with variable workloads is an uptime, scaling, and reliability problem. By 2026, you’ll see dedicated orchestration layers for agents: routing, rate-limiting, sandboxing, observability, rollback. The battle-tested stacks will come from teams that spent 2024–2025 discovering how quickly naive agent frameworks fall apart at scale.
Third, governance. Once you have self-modifying, self-propagating systems, “who changed what?” stops being a philosophical question and becomes an audit requirement. The serious deployments will log agent decisions, policy checks, and self-corrections as first-class artefacts, not as a side effect.
The impact inside the enterprise will be uneven but sharp. Certain workflows will flip from human-centric to agent-centric: incident response, regression hunting, QA, back-office reconciliations, complex routing and triage. Humans will still set objectives, handle edge cases, and own accountability, but most of the glue work between systems will be done by these invisible teams of agents grinding away in the background.
The headline for 2026 isn’t “everyone has an AI coworker.” It’s that a small number of critical workflows will be run end-to-end by multi-agent systems that quietly outperform the old model: fewer outages, faster resolution, tighter feedback loops. The organizations that win this phase won’t be the ones with the flashiest agent UI; they’ll be the ones that treat agent teams as real production systems with infrastructure, simulations, and governance to match.
