From Solo Agents to Team Orchestration: Making Multiple AI Agents Actually Work Together

More developers are running several AI tools at once now: Claude for code, GPT for docs, a specialized agent for data analysis. Each one does its job and it looks productive.

But that kind of parallelism is just more hands on deck, not real collaboration. The moment you try to get multiple agents to handle a task with dependencies, complexity jumps to a completely different level.

Who Sees What

The first question that comes up when agents work simultaneously is who sees what. Two agents writing the first and second half of a proposal; do they need to see each other's progress, or work blind and let a human merge the output? If they can't see each other, how do you keep style consistent and avoid duplication? If they can, how do you handle interference, when agent A reads B's output and gets pulled in a different direction?

There's no universal answer, it depends entirely on the task. Brainstorming needs agents to see each other's ideas and build on them; pipeline tasks only need upstream output, anything extra is noise. What you need is configurable visibility control with different information topologies for different task modes. Most existing frameworks haven't built this abstraction, so developers patch it together in their prompts.

Handing Off Results Between Stages

When tasks span multiple stages, handing off results gets tricky. Agent A writes code, agent B reviews it, B finds issues and sends it back to A, A fixes it and resubmits to B, B approves and passes it to C for documentation. That flow needs a clear state machine tracking which stage the task is at, who holds the current version, who's waiting on input.

Traditional workflow engines handle human tasks where state changes are slow, a stage might take hours or days. Agents execute much faster, running through a dozen steps in minutes, with state changes happening far more frequently than in human workflows. You also get race conditions when agents work in parallel: two agents editing the same document simultaneously, or making decisions based on the same stale information and producing contradictory results. And in traditional workflows every node has human confirmation; if agents just pass results downstream without acceptance checks, low-quality output flows through and errors get amplified along the chain.

The Missing Orchestration Layer

Most multi-agent frameworks solved the problem of starting multiple agents, but not how to get them to cooperate. Several agents running at once; how do they coordinate, shared folders, message queues, or direct conversation? In practice developers write a lot of glue code to manage agent interactions, none of it standardized, all of it rewritten for each new scenario. Debugging is even harder: when multiple agents produce unexpected output, pinpointing which stage went wrong is difficult because each agent's internal reasoning is opaque and interaction logs are scattered everywhere.

The orchestration layer needs to define different collaboration modes. Agents debating a topic with a human making the final call, that's roundtable mode. One agent doing the work and handing it to the next for review, rejection means rework, that's critic mode. A large task broken into subtasks executed in parallel then merged, that's split mode. Each mode has different information flow, permission boundaries, and acceptance mechanisms; one default behavior can't cover them all.

Octo provides six orchestration modes at the framework level: Solo (independent execution), Roundtable (group discussion), Critic (review workflow), Pipeline (sequential handoff), Split (task partitioning), and Swarm (collective intelligence). Each mode has its own visibility rules and state transition mechanisms. Developers pick the mode that fits the task instead of hardcoding collaboration logic for every scenario. This turns orchestration from glue code into configurable infrastructure.

Identity and Permission Boundaries

Each agent should have clear identity and capability boundaries. An agent configured for code review shouldn't be editing product requirements; a data analysis agent shouldn't have permission to touch production databases. Permission management in multi-agent environments is much more complex than traditional IAM: traditional systems have relatively static user identities with clear role-permission mappings. Agent identities may shift with task context; the same agent might be an executor in project A and a reviewer in project B, with permissions that need to follow the scenario.

When an agent acts on behalf of a team member, should it inherit that person's authorization scope or have an independent permission model? There's no industry consensus yet, but it will need to be addressed. Octo's approach treats agents as digital workforce extensions of their creators, inheriting authorization and carrying taste preferences to complete work, with identity and capability boundaries clearly marked through AgentCard.

Try It Out

Spinning up a few agents is easy. Getting them to cooperate reliably and produce traceable results requires a lot of infrastructure. Information flow, permissions, state management, acceptance checks, traceability, each dimension needs careful design.

Octo is now fully open source on GitHub, with server, web/desktop client, iOS, Android, and CLI codebases, under Apache 2.0. If you're exploring the engineering path for multi-agent collaboration, pull the code and try it out. Deployment docs are in the octo-deployment repo with K8s deployment manifests ready to go. The community is just getting started; if this direction seems valuable, drop a star and your early feedback will directly shape where the product goes.