What are the best tools for managing AI coding agents?

Summary

• The right tool depends on what you need to manage: several agent harnesses on one codebase, spend, risky actions, or a team working in the same sessions.
• In open source, Omnigent is built for that layer. It is an Apache 2.0 "meta-harness" over Claude Code, Codex, Cursor, OpenCode, Hermes, Pi and custom YAML agents, and its README says "Status: alpha".
• Omnigent policies return ALLOW, ASK or DENY and ship builtins for spend caps, tool-call limits and approvals, at server, agent or session level. An OS sandbox, shared sessions and forks round it out.
• On Databricks, Omnigent is in Beta as a managed service with workspace identity, model access through Foundation Model APIs and Unity Gateway, and Databricks Sandbox hosts.

What are the best tools for managing AI coding agents?

Start from the job. Managing coding agents usually involves four things: running more than one agent harness without rebuilding your setup, limiting what agents spend and touch, getting a person's approval before risky actions, and letting a team watch or steer the same work.
In open source, Omnigent is built for that layer. Its README describes "an open-source meta-harness that gives you a common orchestration layer over Claude Code, Codex, Cursor, OpenCode, Hermes, Pi, and the agents you write yourself". It is Apache 2.0 licensed and labeled "Status: alpha". Its FAQ says teams use it for real work today, and that releases ship often and can include breaking changes, so pin a version.

What should a management tool handle?

Need	What to check	What Omnigent documents
Several harnesses	Mix agents without rewriting them	Claude Code, Codex, Cursor, OpenCode, Hermes, Pi and custom YAML agents in the same session
Spend	Caps per session, per user or per period	cost_budget, user_daily_cost_budget and user_period_cost_budget builtins
Risky actions	Pause for a person's approval	ASK verdicts, such as ask_on_os_tools before any file or shell operation
Isolation	Limit files, network and secrets	Omnibox OS sandbox: bubblewrap on Linux, Seatbelt on macOS
Teamwork	Share, co-drive and fork sessions	Share with Read or Edit, omnigent attach, omnigent run --fork

How does Omnigent coordinate several agents?

Each agent is a short YAML file, and the executor.harness field picks the runtime. The Databricks docs put it this way: "swap one line to change its harness or model while your tools, prompts, skills, and policies stay the same."

name: my_agent
prompt: You are a helpful data analyst.

executor:
  harness: claude-sdk

Run it with omnigent run path/to/my_agent.yaml. Sub-agents are tools of type: agent, and each sub-agent picks its own executor.harness and model, so an orchestrator can mix harnesses by role. The bundled Polly example shows the pattern: she plans, delegates work to Claude Code, Codex or Pi sub-agents in parallel git worktrees, routes each diff to a reviewer from a different vendor than the one that wrote it, and you merge.
Support levels differ by harness: Claude Code and Codex are "Fully supported", Cursor, OpenCode and Pi are "Maintained", and Hermes is "Community-supported". Most coding agents can run in direct mode or in native TUI mode (ids ending in -native), which boots the vendor's own terminal UI in a pane and mirrors it back.

How do you control what agents spend and do?

Policies check every action and either allow it, block it, or pause to ask you first. They stack across three levels, "server-wide (admin), per-agent (developer), and per-session (you)", with the stricter session rules checked first. The README's example:

policies:
  approve_shell:
    type: function
    handler: omnigent.policies.builtins.safety.ask_on_os_tools
  cap_calls:
    type: function
    handler: omnigent.policies.builtins.safety.max_tool_calls_per_session
    factory_params:
      limit: 50
  budget:
    type: function
    handler: omnigent.policies.builtins.cost.cost_budget
    factory_params:
      max_cost_usd: 5.00
      ask_thresholds_usd: [3.00]

At max_cost_usd, cost_budget blocks models, and expensive_models decides how hard that stop is. With a list, only those model tiers are blocked, so a person can switch to a cheaper one with /model. Without a list, as in the example above, every model is blocked at the limit. user_daily_cost_budget applies the same gating to one person's spend across all their sessions for the UTC day, and user_period_cost_budget does it for a week, month, quarter or year. For isolation, the OS sandbox keeps the working directory read-only until you grant write_paths, masks top-level dotfiles, and can send HTTP(S) traffic through a default-deny egress_rules allow-list.

How do teams share the work?

Hit Share in the web UI to give a teammate Read access (watch the session) or Edit access (co-drive and send messages). Edit lets a teammate run code on the host, so share Read when you can. From a terminal, a teammate can co-attach with omnigent attach <session_id>, and their messages execute on your machine. omnigent run --fork <session_id> clones a conversation so someone can continue independently from the fork point.

How Databricks approaches agent management

Omnigent on Databricks is in Beta. Databricks operates the Omnigent server and integrates it with your workspace's identity provider. Model access goes through the Foundation Model APIs and Unity Gateway, and Databricks Sandboxes can host agents in select AWS regions. Sessions appear at <workspace-url>/omnigent and can be shared with other workspace users. On a Databricks Sandbox host, model usage "inherits Unity Gateway's governance and controls." Policies on Databricks are the built-in contextual policies plus custom policies written in CEL; custom Python policies aren't supported.

FAQs

Can I keep the agent CLI I already use?

Yes. omnigent claude starts Claude Code "in a session your team can join", and omnigent codex, omnigent cursor, omnigent opencode, omnigent hermes and omnigent pi launch the other runtimes. Native TUI mode keeps the vendor's own terminal interface.

Do I need Databricks to use Omnigent?

No. The FAQ says "Omnigent is open source and runs on your own machine and models." A Databricks workspace is one supported model provider.

How do I install it?

One curl command runs the installer, or you can run uv tool install omnigent or use pip. Omnigent needs Python 3.12 or newer.