AI coding agents
The phrase "AI coding agent" now covers three quite different things, and conversations go wrong when people mean different ones.
This page separates them, then covers the practical problems that show up once agents run without a human watching each step: cost, identity and secrets.
Three kinds of coding AI
Completion tools
Suggest the next lines as you type. You are writing the code; the model is predicting. GitHub Copilot's original autocomplete is the archetype.
Coding agents
Take a task, plan it, edit multiple files, run tests and iterate — inside a codebase that already exists. Claude Code and Cursor's agent mode sit here. You review the result.
Agent workforces
Multiple specialised agents with distinct responsibilities operating a product over time, not a single task. Empyre's eight agents — CEO, CTO, CMO, CFO, CSO, CLO, Creative and Tester — are this shape.
Why a workforce rather than one agent
A single agent given “run this business” has no structure to push back against itself. Splitting responsibilities creates useful friction: the agent that wants to spend is not the agent that approves spend.
In Empyre that is literal. The CTO wants to deploy; the CFO gates the cost first. The CTO believes the build works; the Tester exercises it and files what broke. Neither can quietly overrule the other, because they are separate cycles with separate prompts and separate authority.
The three problems every agent system hits
Cost runs away
An agent that can call a model in a loop can spend real money in a loop. The fix is a gate before the work, not a report afterwards. Empyre paces AI spend daily so one day cannot consume the month, and refuses work that would breach its limits.
Identity — who is the agent acting as?
An agent acting for a user should not hold that user's password, and should not have permissions broader than the task. That is an OAuth problem, and it is what Relay solves: hosted consent, PKCE, scoped tokens, rotation and revocation, purpose-built for agents rather than retrofitted from a human login flow. More on OAuth for AI agents →
Secrets — where do the keys live?
Agents need API keys, and an agent that holds a raw key can leak it into a log, a prompt or a commit. Vault keeps keys encrypted and returns access or a signature instead of the secret itself, recording every sensitive action. More on secrets for AI agents →
Frequently asked questions
What is the difference between an AI coding agent and an AI app builder?
A coding agent works inside a codebase that already exists, making reviewable changes. An app builder starts from nothing and produces a whole application. Both can be right; the deciding factor is whether there is already code.
How do I stop an AI agent spending too much?
Put a hard limit in front of the work rather than a report behind it, and pace it over time so a single runaway day cannot exhaust the period. Empyre does both: spend limits enforced by a dedicated CFO agent, plus daily pacing that blocks further work once the day's share is consumed.
Can AI agents deploy to production safely?
With gates, yes. Empyre blocks deployment until the core product is marked working, keeps paid advertising locked for 21 days after launch, and runs a Tester agent whose only job is to find what the CTO broke. Ungated autonomous deployment is the part to be careful about, not autonomy itself.
Which model runs Empyre's agents?
Every agent cycle runs on a frontier model, and the model choice is deliberately not a product surface — it is an implementation detail that changes as models improve, so it is not exposed in the dashboard or the API.
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