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Agent Architecture
Intermediate

From CLI Harness to Product

Grow a working agent loop into a product: sessions, config, resumability, and the packaging polish the successful terminal agents share.

5 steps15 verify checks90-150 minutesWorks with: emergent · chatgpt · claude · cursor
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The Route

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Context Pack

Paste this first. It briefs the AI on requirements, constraints, and the Definition of Done before your first build prompt.

Context Pack
PROJECT CONTEXT:
I have an agent loop that works in a script. The gap between that and something people adopt - session management, config, resume, distribution - is exactly the gap the polished terminal agents crossed.

GOAL:
Productize my agent harness: named sessions with resume, layered configuration, first-run experience, and clean packaging.

REQUIREMENTS:
- Persistent sessions: every run saved, listable, and resumable by ID
- Layered config: defaults < config file < environment < flags
- A first-run flow that validates credentials and writes a starter config
- Clean CLI surface: run, resume, list, config commands with helpful errors
- Distribution as an installable package with a versioned changelog

CONSTRAINTS:
- Sessions must store enough to truly resume (history, todo state, cwd), not just a chat log
- Secrets never written into session files or logs

DEFINITION OF DONE:
- `agent resume <id>` continues a previous task with full context restored
- Config precedence works and `agent config` shows the effective merged values
- A new user reaches a successful first run in under 2 minutes
- The package installs cleanly on a fresh machine and reports its version

COMMON FAILURES TO AVOID:
- Resume that replays a chat log but loses tool state and working directory
- Config spread across hardcoded values nobody can override
- First run failing cryptically on a missing API key
- Session files leaking API keys into plaintext on disk

Paste this into your AI builder first. It teaches the AI what you want before you give it the build prompt.

Related routes

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Add a Plan/Todo System to Your Agent

Give your agent the planning discipline of the leading harnesses: an explicit, model-visible todo list that survives long tasks.

01Design the todo data model and tool
02Render the plan into context every turn
03Enforce plan-first and completion gates

+1 more steps to Done

Best forbuilders who have shipped a basic app before

4 steps60-90 minutesIntermediate
Flows category icon
Agent Architecture

Agent-to-Agent Protocols (A2A)

Connect two agents over an explicit protocol - task handoff, acknowledgment, and failure handling - instead of hoping chat works out.

01Define the protocol
02Build the message layer
03Wrap agents as protocol endpoints

+1 more steps to Done

Best forproduction-grade builds with strict verification

4 steps90-150 minutesAdvanced
Flows category icon
Agent Architecture

Assemble a System Prompt Like the Pros

Structure your agent's system prompt the way leading harnesses do: layered sections, dynamic context, and enforceable rules.

01Decompose the current prompt
02Build the section-based assembler
03Pair rules with enforcement

+1 more steps to Done

Best forbuilders who have shipped a basic app before

4 steps60-90 minutesIntermediate

Made with Emergent