Intent vs Conductor: macOS Multi-Agent Coding Orchestrators Compared

Intent provides more capable spec-driven multi-agent orchestration by adding living specs, coordinator-led task decomposition, and codebase-aware context on top of git worktree isolation. Conductor focuses on lightweight parallel execution and review.

What These Tools Actually Solve

Consider two developers. The first maintains a Node.js monorepo with 12 services and wants to run parallel refactoring tasks across the authentication, billing, and notification services simultaneously. The second is a solo developer on a greenfield project who wants three Claude Code sessions running in parallel without manually managing terminal panes and git branches.

Conductor solves the second problem cleanly. Instead of opening three terminal tabs, creating worktree branches by hand, and tracking which session is working on what, Conductor gives you a UI that handles workspace creation, agent assignment, and branch management in clicks. For developers already comfortable defining their own tasks, that's the entire friction point removed.

Intent solves the first problem. When refactoring touches 12 services with shared configuration and cross-service dependencies, the hard part is deciding what to change where and in what order. Intent's Coordinator Agent reads the codebase, drafts a spec, decomposes it into tasks with dependency ordering, and delegates to specialist agent personas that handle investigation, implementation, and verification separately. That orchestration layer adds complexity, but for multi-service work it replaces the planning you would otherwise do manually.

Most developers reading a comparison article are closer to Conductor's use case than Intent's. If you are not already working with specs or managing cross-service coordination, start with Conductor and evaluate Intent when your codebase or team complexity demands it.

Here is what I found across seven comparison dimensions.

Agent Flexibility: BYOA vs Two-Agent Support

Both tools let you run coding agents in isolated workspaces, but they differ in how many agents you can bring and how deeply the orchestration layer integrates with them.

Intent supports four agents: its native Auggie agent plus three external agents through its BYOA (Bring Your Own Agent) model, including Claude Code, Codex, and OpenCode. Conductor supports Claude Code and Codex according to its FAQ.

All agents in Intent can access codebase-awareness features via the Context Engine, not just the native Auggie agent. Conductor adds and manages codebase-level context through persistent project documentation that augments the underlying agents' native context.

Intent's BYOA model gives you the option to assign different agents to different tasks within the same orchestrated workflow. Without a documented reason to mix agents on a specific project, Conductor's simpler model has less friction and less to configure.

Workflow Structure: Living Specs vs Issue-Driven Execution

Workflow structure drives the biggest product difference. Intent centers work on a living spec; Conductor centers work on developer-defined sessions. Intent decomposes tasks automatically, while Conductor leaves decomposition to the developer.

Conductor starts from external inputs: a Linear issue, a GitHub branch, or a manual branch name. The developer defines the task, assigns it to a workspace, and the agent executes.

Intent introduces a coordination layer that Conductor does not have. The Coordinator Agent analyzes the codebase, drafts a spec, generates tasks, and delegates them to specialist agents with built-in personas: Investigate, Implement, Verify, Critique, Debug, and Code Review. This multi-agent orchestration model means the spec stays alive as agents complete work. It reflects what was actually built rather than drifting from the original requirements.

Intent's coordinator and specialist model creates subtask boundaries automatically, while the Verify agent acts as a quality gate before merge. Conductor requires the developer to define those boundaries and review each agent's output independently.

The abstraction cost here deserves direct attention. When the Coordinator decomposes your spec, you lose fine-grained control over which agent works on which file and in what order. You can stop the Coordinator at any point to manually edit the spec or adjust the task breakdown before agents continue, but you are reviewing and correcting the coordinator's plan rather than writing your own from scratch. If the coordinator misjudges a decomposition (splitting work across a service boundary that should have been handled atomically, for example), the error propagates to specialist agents before you catch it. Teams that already have strong task-definition practices may find this layer adds overhead without a corresponding return. Teams that struggle with decomposition across large codebases will find it saves significant planning time.

One caveat on living specs: the spec auto-updates to reflect what agents actually built. If an agent implements something incorrectly, the spec updates to match the incorrect implementation. The Verify agent is designed to catch these mismatches, but the spec itself does not independently validate correctness. You still need to review the final output.

For teams evaluating spec-driven development workflows, Intent is the more opinionated system.

Worktree Isolation: Both Isolate, Differently

Each agent runs in a separate git worktree in both tools, but the merge and review mechanics differ significantly.

Conductor uses isolated workspaces documented as separate copies and branches of your repo, with setup scripts available to copy untracked files and run dependency installs in each workspace. The practical friction point remains that dependency installs may need to run fresh in each workspace.

Intent uses coordinator-managed worktrees as part of its agentic development environment, with multiple agents running in parallel.

The collision problem is where parallel agent execution gets difficult in practice. Merge conflicts escalate when agents modify shared files simultaneously, and certain file types are disproportionately affected: routing tables, configuration files, component registries, and shared type definitions. These files act as collision hotspots because nearly every feature change touches them. When two agents modify the same config file in separate worktrees, the merge produces a conflict that requires manual resolution, and the agent that completed second has no awareness that its changes will collide. Neither tool currently detects these collisions before they happen. Intent's documentation describes conflict guidance and automated checks pre-merge; Conductor provides a diff viewer for reviewing changes. But in both cases, the developer is the final arbiter when worktrees touch shared files.

Neither tool solves the frontier problems identified by Upsun's worktree analysis: per-worktree environment isolation including databases, Docker daemons, and services. Neither tool's documentation describes predictive file-collision detection before spawning parallel agents.

Because claims about parallel worktree productivity vary heavily by team and task shape, treat coordination-overhead arguments as directional guidance unless backed by product-specific benchmarks.

Codebase Context: 400,000+ Files vs Agent-Native Windows

Intent and Conductor take fundamentally different approaches to codebase understanding. Intent provides repository-scale semantic analysis through its Context Engine; Conductor stays at the session-orchestration layer and relies on the underlying agents' native context windows.

When using Auggie, Intent's Context Engine semantically indexes relationships across hundreds of thousands of files, building dependency graphs and call-flow analysis. This codebase awareness feeds directly into Intent's orchestration: the Coordinator Agent uses it when decomposing specs into tasks, and specialist agents use it when implementing changes that span multiple services. For external validation of coding performance, the SWE-bench leaderboard is public, though that benchmark does not independently validate the specific 400,000+ file processing claim.

The scale at which this matters is worth grounding. On a 5,000-file greenfield project with a single service, Claude Code's native context window is likely sufficient and the Context Engine adds indexing time without a proportional payoff. The gap widens as codebases grow: a 50,000-file monorepo with cross-service dependencies starts to exceed what any single agent context window can hold, and a 400,000-file enterprise codebase with years of accumulated architecture makes repository-scale indexing the difference between an agent that understands your system's constraints and one that generates plausible but architecturally wrong code.

Published code review benchmarks support the broader claim that repository-scale context contributes to code quality, even though these are not direct benchmarks of Intent itself.

Conductor's architecture does not include a dedicated codebase context layer. It augments whatever context Claude Code or Codex provides with its own structured context layer, including system prompts, workspace metadata, and an isolated copy of your codebase. Conductor positions itself as a tool for orchestrating teams of coding agents, not as a context enhancement tool.

BYOA users in Intent get the orchestration layer and can also enable the Context Engine via Context Engine MCP for codebase-aware features, though the depth of integration may differ from the native Auggie experience.

PR Workflow and CI Automation

Both tools move work toward GitHub review, but the level of automation differs. Intent provides app-level git operations; Conductor guides the developer through next steps.

Intent's PR workflow includes integrated git and PR capabilities: users can inspect diffs, create PRs directly from the app, and move from prompt to commit to PR to merge without leaving the workspace. PR descriptions are auto-filled from the living spec, so reviewers see context about what changed and why.

Conductor's PR workflow uses Suggested Git Actions, which recommend the next step via a button in the UI. This approach provides guided automation for PR creation. Conductor also lets users open a workspace from Linear issues.

Neither tool has built-in CI fix automation documented in current public materials. This gap compounds with parallelism: if you run three agents on separate features and each generates a PR that fails CI once, that's three manual context switches in a single feature cycle. Each round trip involves reading CI output, diagnosing the failure, switching to the correct worktree, applying the fix, and re-pushing. At three agents, you might spend as much time fixing CI failures as you saved by parallelizing the work. GitHub's agentic workflow documentation describes what a full CI auto-fix loop looks like: an agent reads failure output, applies a fix, reruns validation, and repeats. Until one of these tools closes that loop, the productivity gains from parallel agents are partially offset by the serial cost of fixing their CI failures.

Enterprise Readiness: Certifications vs Free Simplicity

For engineering managers running procurement evaluations, enterprise readiness may be the decisive dimension. Intent documents formal compliance certifications; Conductor emphasizes local storage and simplicity, creating a lighter-weight trust model.

Augment Code holds SOC 2 Type II compliance and ISO/IEC 42001 certification as documented across its product materials and blog. I did not find a public certificate registry entry or downloadable audit report in the sources used here, so this remains a vendor-documented compliance claim rather than a directly inspectable primary audit artifact.

Conductor saves chat history locally in the Application Support directory and does not store it on its servers. Conductor collects analytics such as feature usage and crash logs. Conductor does not publish formal compliance certifications (SOC 2, ISO 27001, or similar) in its current public documentation for conductor.build.

Conductor's lack of team licensing and organizational features is a hard stop for any team evaluation. If you need to manage seats, pool credits across developers, or demonstrate compliance to a procurement review, Conductor does not currently offer the infrastructure for it. For individual developers evaluating tools for personal use, this is irrelevant. For engineering managers evaluating tools for a team, it eliminates Conductor from consideration on organizational grounds before feature comparison even begins.

Pricing: Free vs Credit-Based

Conductor adds no software fee; Intent uses a credit system tied to Augment Code plans.

Conductor's public materials do not publish detailed pricing, but they indicate it uses the user's existing Claude Code login or plan. You pay for the underlying model usage (API usage with Anthropic or OpenAI directly); some documentation also states there is no premium added to third-party model inference. Conductor uses passthrough authentication from your existing Claude Code session.

Augment Code uses a unified credit system:

Credit consumption ranges from roughly 300 credits for a small task to roughly 4,300 credits for a complex task, according to published pricing materials. That 14x range is a real budgeting concern. A team on the Standard plan with 130,000 pooled credits could burn through their monthly allocation in 30 complex tasks or stretch it across 400+ small ones, and the difference depends on codebase size, task complexity, and how many specialist agents the Coordinator delegates to. Teams evaluating Intent at the Standard tier ($60/dev) should run a pilot month to understand their actual consumption pattern before committing.

For an individual developer, the question is simpler: $20/month for Intent's Indie plan buys you the Coordinator Agent, living specs, and Context Engine on top of what Conductor offers at zero incremental cost. If your work involves multi-service coordination or codebase-wide refactoring, the orchestration layer justifies the spend. If you are running parallel Claude Code sessions on well-defined tasks, Conductor delivers the core workflow without the fee.

Put Spec-Driven Multi-Agent Orchestration to Work

The decision comes down to one question: are you managing cross-service coordination from a spec, or running parallel sessions on well-scoped tasks?

Most developers evaluating multi-agent orchestrators are closer to Conductor's use case. If your team is smaller than five engineers and does not already follow a spec-writing discipline, Intent's coordinator layer adds cognitive overhead (six specialist personas, living spec management, credit budgeting) without a proportional return. Start with Conductor.

Intent becomes the stronger choice when three conditions converge: your codebase is large enough that cross-file dependency awareness matters (roughly 50,000+ files or multiple interconnected services), your team already works from specs or PRDs, and you need enterprise compliance documentation for procurement. When those conditions hold, the Coordinator Agent, living specs, and Context Engine solve coordination problems that Conductor's manual approach cannot.

FAQ

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