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This track is for developers and AI platform teams. You work in the coding harness your engineers already use, such as Codex, Claude Code, or the Gemini CLI, and build compound agents at the code level. The non-technical track builds skills and simple agents. This track builds the compound ones.

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The Agent Development Kit

The Agent Development Kit (ADK) defines the build sequence from a business objective to a tested, deployed agent. Each stage uses current Company Brain evidence where the process is relevant to the decision.
You do not install a separate ADK package. The method is served through the MCP. Once Within is connected in your harness, the assistant calls get_agent_builder_instructions and follows the returned method, retrieving the supporting resources it names.
Because the method is served at build time, start each new build by retrieving the current instructions. Version the code, configuration, evaluations, and evidence baseline that the build produces in your own repository.

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The sequence

The assistant guides the sequence and produces the build artifacts in your harness. Your team supplies the objective, constraints, approvals, and technical judgment. Company Brain evidence grounds process claims, but it does not replace security review, system documentation, or runtime testing. The prompts below are what a builder types in their harness with Within connected.
1

Objective

Define the business objective and the technology stack the solution should live within. Giving both the goal and the stack constraints up front makes recommendations far more useful and deployable.
See Set the objective for what a well-formed one looks like.
2

Companion

Make sure the Company Brain covers the area in question. Where coverage is thin, deploy Companion against it before building.
3

Analyze

Produce a current-state read from observed data: pain points, bottlenecks, time and motion, systems in use, and bright spots.
4

Recommend and select

Generate candidate use cases and screen them against evidence, expected impact, feasibility, and required controls.
Apply the screens in Pick the right use case. The Find transformation opportunities guide covers the tool chain behind this step.
5

Design

Produce the agent specification, effectively a PRD for the agent: scope, governance, credentials, and flow.
6

Build

Generate the agent: code, skills, and configuration.
7

Test

Generate and run evaluations before release. Include ordinary cases, edge cases, malformed or missing inputs, and requests the agent should refuse.
8

Deploy

Ship into your environment.

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What you build

Compound agents, managed, orchestrated, or custom SDK-built, and deterministic Integration Flows. See What you can build for how the primitives differ and when each one fits. Two controls run throughout. Material process claims cite observed data, and a person approves the design and release gates.

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Artifacts are part of the value

The current-state read and recommendation can be generated as visual HTML artifacts. Use them to review the evidence, assumptions, controls, and proposed change with stakeholders before implementation.

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Keeping agents current

An agent can drift as the process changes. See Keep agents current for how to compare an existing build with current Company Brain evidence and decide what needs to be retested.

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Within support

Within’s team can work with your engineers on implementation, production readiness, and evaluation design.

Agent Builder tools

The two tools that serve the methodology.

Build skills, agents, and automations

Use the same flow from any MCP client.