The Context Engine for the Agentic Development Lifecycle
Your agents should know what your team knows. One Meko datapack holds your coding standards, decisions, and learnings for a repo, so every agent starts with the team’s full context.
The problem
Every agent session starts cold
Most teams keep agent learnings in a markdown file per repo, and each new session re-derives what the team already figured out. Adding more context does not fix this.
Too little context
The model has to guess and quality suffers.
Too much context
Quality dilutes while cost climbs.
Meko context
Meko structures context, tracks where it came from, and only sends the relevant subset to the model. Quality is maintained as context grows.
What ADLC is
The agent drives. You approve.
The Agentic Development Lifecycle starts with intent and guardrails. An agent runs the stages end-to-end, and you review the result at the gate. The stages are the same as the Software Development Life Cycle (SDLC) you already know. What changes is who starts the work and where you sign off.
| AI-assisted SDLC Level 3 | ADLC Level 4 · Today | Software factory Level 5 · Direction | |
|---|---|---|---|
| WHO STARTS IT | You, one prompt at a time | You, with intent and guardrails | A queue. Nobody prompts |
| WHO DOES THE STAGES | You, with AI helping on a step | One agent, end to end | Many agents, in parallel |
| WHERE YOU SIGN OFF | Every step | The outcome, at the gate | Above a trust bar, exceptions only |
| THE TELL | You are in the editor all day | You are reading PRs an agent opened | You are watching a queue drain |
How Meko supports ADLC
One datapack per repo, shared by every agent
One source of context for the project
A datapack holds a project’s conversations, memories, knowledge, decision traces, and dynamic project data. Claude, Cursor, Codex, a custom agent, and your teammates all read from the same pack at once over MCP, the UI, or the API.
Context for every stage of the loop
Spec, build, review, merge, and docs all draw on the same pack. Your agent pulls your coding standards when it writes code, and cites the rule when it flags a problem in review.
You set the guardrail line
Choose per stage how far the agent goes: draft only, post and ask, or auto-approve above a trust bar. The decision trace is kept either way. Move the line as the agent earns trust.
With approval, learnings become team knowledge
What an agent discovers stays in its private memory until a human approves it, with evidence, into shared context. From there every future agent uses it. Nothing is promoted silently.
Every answer traces back to the source
Provenance is tracked automatically, so you can see where each piece of context came from and who introduced it.
Friday’s agent remembers what Monday’s team learned.
Get started
Quick Start With Meko
Create one datapack per repo
Seed it with your coding standards, docs, Slack threads, and meeting transcripts.
Connect your coding agent
Generate an API key, install with one npx command, and restart your client. You need Node 20.18 or later.
Write back issues
Every one you fix is one less for the next agent to encounter.
Move the guardrail
As findings prove reliable, let the agent go further on its own.
Try it now: Run our hands-on lab, where an open-source PR reviewer called AgentK uses your datapack to review a pull request against your rules.