Assemble a team of specialist AI agents.
One generic chatbot will plateau. A roster of focused personas - each with its own instructions, skills, tool bindings and guardrails - keeps shipping. Hire the team your codebase actually needs.
Ships TypeScript features end-to-end.
Runs tests. Returns a pass/fail verdict.
Security and design pattern feedback.
Reads the codebase. Reports findings.
Refactors. Tightens. Cleans up.
Breaks scope into shippable issues.
One agent. Every job. Bad fit.
No role, no rigour
A generic chatbot has no opinion. It will refactor when you asked for review, and chat when you asked for code. Specialisation is what makes the output reliable.
Skills don’t compose
Stuff every standard, framework rule and tool into one mega-prompt and quality collapses. Personas split that knowledge by role - and only inject what the step needs.
Tools are unbounded
Without persona-level guardrails, every agent gets every tool. That’s how a doc writer accidentally runs production migrations. Bind tools, scope memory, ship safely.
Meet the roster.
Click through five canonical personas. Same anatomy - different instincts, different skills, different tools. Each one is a typed, versioned, reusable identity.
Ada
Primary coding agent. Ada accepts a prompt, opens a fresh sandbox, writes the code, runs the dev loop and produces a diff. She prefers explicit types, functional patterns and small commits.
Read/write scope: project repo only. No production secrets. Auto-stops on lint or type errors.
- TypeScript96
- React + Next.js92
- Test-driven dev88
- Sandbox tooling86
- Conventional commits78
- Diff hygiene82
Anatomy of a persona.
A persona is more than a system prompt. It’s a typed bundle of identity, skills, tools, memory, output contract and guardrails - checked into your project and versioned like code.
Hire from the roster. Or design your own.
CodeCourier ships with the canonical persona types. Need something off-script - a migration assistant, a changelog writer, a domain-specific reviewer? Define a custom persona in minutes.
- 1Define the role and tone
- 2Pick a model + thinking effort
- 3Assign skills + tool bindings
- Set guardrails and ship
Personas compose from a skill library.
Each skill is a tight bundle of domain knowledge - patterns, gotchas, idioms. Drop a skill onto a persona and the agent suddenly knows your stack the way a senior would.
TypeScript expert
Strict types, generics, exhaustive switches. Treats `any` as a code smell.
Go expert
Idiomatic Go: error returns, table-driven tests, context-aware APIs.
Python expert
Modern Python: type hints, dataclasses, async-first IO, ruff-clean code.
SQL surgeon
Indexes, query plans, window functions. Sniffs out N+1 from the schema.
Code reviewer
Security, maintainability and clarity heuristics. Cites lines, not vibes.
API designer
REST + GraphQL conventions, pagination, error contracts and versioning.
Docs writer
Plain-English explanations, ADRs, READMEs that actually onboard people.
DevOps
Containers, CI gates, observability - pipelines that survive a real outage.
Define personas from your editor.
Anything the UI can configure, the SDK can. Personas are typed TypeScript - review them in pull requests, diff them across versions, ship them with your code.
// Senior TypeScript reviewer - read-only, no merges
import { definePersona } from "@codecourier/sdk";
export const knuth = definePersona({
name: "Knuth",
role: "reviewer",
model: "claude-opus-4-7",
thinkingEffort: "high",
skills: ["security", "design-patterns", "api-design"],
tools: ["diff", "symbol-search", "repo:read"],
guardrails: {
writes: false,
network: "deny",
maxRuntime: "15m",
},
instructions: "Cite file:line for every finding. Refuse to merge.",
});
await knuth.publish(); // → v4 activeA senior TypeScript reviewer persona - model, skills, tools and guardrails - defined in a dozen lines.
Bind personas to workflow steps.
A workflow is a sequence of steps. Each step is powered by exactly one persona. Swap personas without rewriting the workflow - and swap workflows without rewriting the personas. This is how a team of specialists actually composes.
Every run records which persona executed which step, on which sandbox, with which version. Per-persona analytics - success rate, cost, average iterations, quality score - follow automatically.
Three rosters worth stealing.
Frontend Team
When the UI sprint moves fast and the component library cannot regress.
Backend Team
When you’re refactoring a service and every diff has to survive a senior code review.
DevX Team
When platform work mixes scaffolding, docs and tool ergonomics across many repos.
We stopped sharing one assistant across the team and started designing specialists. Three weeks later our review backlog was zero and the senior engineers had time to actually design the system again.
What engineering leads ask.
Can I evolve a persona over time?
Are personas just system prompts?
How are tools sandboxed?
Can personas share memory?
What’s the difference between a persona and a workflow?
Hire your first AI engineer.
Ship by lunchtime.
5 minutes to onboard. First PR within an hour. Cancel anytime.