Updated on October 12, 2026. The analysis pilot is preserved and distinguished from the subsequent multiuser deployment stage.
The second-stage leap
The backup answered what code existed. It did not explain how the business was organized, which functions made up a flow or how to check that a fix had been applied.
The second stage added three pieces:
Piece
What it answers
Resource dictionary
Which functions use each shared resource and what the blast radius is
Process tree
Which flows exist and which functions make up each branch
Analysis contract
How a branch should be analyzed and what evidence to deliver
Generated and curated
Data that changes is regenerated on every sync. The conceptual organization is expressed through rules maintained by people.
No manual lists of functions are written into the documentation: they go stale the next day. A rule absorbs new functions and the report flags those that do not fit any branch.
Unit of work: one branch
The process for a person or agent is deliberately bounded:
Open a working copy of the repository.
Pick a branch from the process tree.
Follow the analysis contract.
Deliver three artifacts.
Deliverable
Purpose
manual-operativo.md
Explain the flow in business terms
mejoras-propuestas.md
Rank findings by consequence, anchored to file and line
validar.sh
Turn every finding into an executable check
In the original test, the agent started from computed reports rather than a blind search across more than thirteen hundred functions. That size describes the inventory at the time, not an updated count. The advantage of the method is bounded context and repeatable analysis.
The limit that sustains quality
The analysis fixes visible defects; it does not redesign processes or propose broad migrations. On a platform where publishing is immediate, a large redesign of a flow that moves money needs another level of governance.
The restriction does not live only in the prompt: every finding that gets developed must be expressible as an automated check. If it cannot be verified, it is noted as a separate idea and not presented as a ready fix.
Verification without AI
When someone reports they fixed a finding, there is no need to pay for reasoning again to read the code. You run validar.sh against the current snapshot.
Prose goes stale; the check gets re-evaluated. This separation reduces supervision and keeps the report from becoming a frozen truth.
From finding to deployable proposal
The new stage adds continuity to the work: a verifiable finding can become a change proposal separate from the snapshot, pass through a pull request and reach an owner with deployment authority. The map helps choose where to look; the contract determines what evidence to deliver; the deployment path governs when writing is allowed.
These are different checks:
Check
What it establishes
What it does not establish
Finding check
Whether the code satisfies a specific condition
Whether the whole process works correctly
Comparison with the production baseline
Whether drift is detected under normalized comparison since the proposal was prepared
Changes erased by normalization, semantic equivalence or unchanged dependencies
Download after deployment
Whether downloaded code matches what was sent under the implemented comparison
Whether business effects are as expected
An idempotency fix, for example, can have a static check detecting its guard condition and still need a repeated-execution test to demonstrate that it does not duplicate effects. Having validar.sh improves review; it does not justify confusing code inspection with comprehensive validation.
The multiuser-stage documentation records controls tested with writes disabled and keeps the first real deployment from cambios/ pending. The full workflow is the direction implemented by the project, with that operational validation still open. The pilot below demonstrates analysis and its deliverables, not a subsequent deployment.
Pilot test
A branch with no previous manual was analyzed following only the contract. The agent delivered the three files and the priority findings were verified against the code.
The test found idempotency bugs, incomplete validations and external execution surface that needed review. The anonymization rules keep the problem categories, but do not publish the flow, the names or the client’s exact conditions.
The analysis also flagged as unconfirmed the points the API did not allow checking. That discipline was part of the success, not a limitation to hide.
Choosing the next step
For an unfamiliar flow, start with the map, contract and checks before preparing changes. Once a finding has evidence, use the function platform to organize the proposal and the separation of authorities to bound publication. A broad redesign or a condition that the snapshot cannot establish needs a different investigation; it should not be disguised as a fix ready to deploy.
The benefit
Diagnostic capability stopped depending on whoever knows the organization by heart. The core cost is generating the snapshot and reports; each team member can analyze a branch with their own tool and verify fixes without production access.
Agent Development Environment: an environment built to run, organize and supervise development agents.
Agent
A system that combines a model, instructions and tools to carry out tasks within a defined scope. Its ability to act depends on the harness, environment and permissions.
Agent-to-agent
Delegation between full agents that work in separate conversations and environments, exchange instructions or files and return results.
Amp
Agentic development environment that brings models, threads, remote machines, Git and coordination together on a single platform.
Benchmark
An evaluation with defined tasks, conditions and scoring rules. It compares results within that protocol; a score does not demonstrate the same performance in your project.
BYOK
Bring Your Own Key: a setup where you supply your own provider API key. Usage follows that account and integration’s terms; it is not the same as using a chat subscription.
Choice
A Jev decision primitive that chooses among application-defined options and returns an option with a probability distribution.
Claude Code
Anthropic’s coding agent, combining project context and tools. Instructions, skills, hooks and subagents can specialize its work, depending on configuration.
CLI
Command-line interface. Lets you work with tools and agents from a terminal, with direct access to the project and its environment.
Cline
A coding agent that can work interactively or through its CLI without an interactive interface. Automation requires a bounded goal, authorized changes and output validation.
Codex CLI
The Codex terminal interface for working on a repository with an agent. The sandbox limits execution, while the approval policy determines which actions require authorization.
Context window
The token capacity a model can handle in an interaction, depending on its configuration. The advertised maximum, actual context usage and information-retrieval quality are different things.
Control plane
The layer used to organize and supervise agents, sessions and execution environments. It coordinates work without being the model or the machine running it.
Cost per completed task
The cost of work that meets its acceptance criteria, including failed attempts, tools and review. It evaluates productivity beyond price per token or session.
Cost per session
Observed spending in an agent session. Comparisons must fix the harness, turn-count range and aggregation; a cheap session may have failed and is not the same as a completed task.
CRM
A system that centralizes relationships, data and commercial processes. In these cases it also acts as the operational source of truth.
Curated memory
A selection of decisions, conventions and discoveries useful to later sessions. Each memory retains context and must be checked against the current state before use.
Daemon
A process that keeps running in the background and sustains services, sessions or tasks even when an interface is closed.
Decision gate
A checkpoint that stops progress until a decision is resolved or a condition is met. Confidence in an answer does not replace required authorization.
DESIGN.md
A portable file that encodes colors, typography, composition and visual rules so different agents produce consistent artifacts.
Engram
A local, persistent memory system for agents that lets them save and search project observations through a CLI, MCP and other interfaces.
Gentle AI
A coding-agent configurator that prepares integrations, memory, skills and workflow or review components to tailor the environment’s governance.
Grant
A Tailscale rule that grants network or application capabilities between defined sources and destinations. Permissions add together; one rule does not subtract access granted by another.
Handoff
A transfer of context and responsibility to another agent or person: goal, changes, tests, limits, open issues and the result’s location. It lets work continue without reconstructing it from scratch.
Harness
The system that manages the agent loop: preparing context, exposing tools, executing calls, collecting results and applying controls. A CLI is an interface to it, not a synonym.
Healthcheck
A service health check. It must validate a signal that represents the expected capability, rather than merely receiving an HTTP response.
Hermes
An operational agent that can live on a server, connect to channels and integrations and run tasks with continuity.
Jev
A TypeSafe model for structured decisions: it takes text or state and returns typed answers using options and criteria defined by the application.
Latency
Waiting time between a request and a response milestone. Specify whether it ends at the first token or the complete response, and under which conditions and percentile.
Least privilege
The principle of granting only the permissions, credentials and network access a task requires. It reduces the impact of mistakes and prevents a broad request from becoming unlimited authority.
Liveness
A signal that checks whether a process is alive. On its own, it does not establish that the service can handle traffic correctly.
MagicDNS
A Tailscale feature that makes tailnet devices discoverable by name. It helps locate a destination but does not grant access.
MCP
Model Context Protocol: an open protocol connecting AI applications to tools and data sources. A connection does not replace permissions or approval for actions.
Median
A measure that divides an ordered dataset into two halves. It describes the center but does not show spread or the cost of extreme sessions. It is not the same as the mean.
Model
A trained component that processes inputs and generates outputs, including proposed tool calls. An agent adds instructions, context, execution and controls around that model.
n8n
A visual automation platform used to coordinate integrations, webhooks and steps across different systems.
Negative test
A check that a forbidden action actually fails. For networking, test against an available service so an outage is not mistaken for an effective restriction.
Noul
A Jev primitive that estimates, between 0 and 1, the probability that a defined condition holds.
Observability
Evidence for understanding what a system is doing: state, output, events and health signals. It distinguishes active work, waiting, blockage and failure instead of relying on an open interface.
ODD
Organic Driven Development: Gentle AI’s approach to exploring, implementing authorized changes, verifying results and leaving recoverable references in proportion to the work’s size.
OpenCode
An open coding-agent tool with configurable models and providers. Catalog flexibility requires checking compatibility, permissions and results for each task.
OpenDesign
A local-first workspace that combines coding agents, skills, templates and DESIGN.md systems to produce visual artifacts as real files.
OpenRouter
A platform that provides access to models from different providers through a common API. Its rankings describe the data included in each view, not the entire market or the quality of every task.
Oracle
A second-opinion role inside Amp, used by the main agent to consult on reasoning, planning or hard decisions.
Orb
A fresh, isolated remote machine where an Amp agent can work with code, tools and services even while the user’s computer is off.
Orca
An ADE and control plane for organizing agents, repositories, workspaces, terminals and hosts without replacing the model.
Orchestration
Coordination of agents through a shared goal, tasks, dependencies, ownership and completion evidence. Parallel sessions alone do not establish who decides or integrates.
Pareto frontier
Options for which no other is at least as good on every chosen dimension and better on at least one, such as cost and score. The frontier changes when dimensions or candidates change.
Preflight
A pre-deployment check that runs the actual production command in a test environment and verifies that the service starts and responds.
Process tree
An organization of business workflows and the functions in each branch. It bounds analysis and defines verifiable evidence for a unit of work.
Provider
A service that receives requests and runs a model. It may differ from the model’s author; the endpoint, limits and performance are part of the configuration being compared.
Read-only
Access that allows inspection without changing the source system. A real boundary is enforced through credentials, tools and architecture, not just an instruction to the agent.
Readiness
A signal that checks whether a service is ready to handle traffic. It can fail even while the process remains alive.
Reasoning effort
A control for a model’s deliberation effort, depending on provider and harness options. It can increase time and usage; it does not replace context, tools or verification.
Routing
Selection of the model or provider that will handle a request, using rules or a router. It changes cost, availability and behavior, so it should be recorded when comparing results.
Runner
A self-owned or managed machine that Amp can use as its execution environment instead of an Orb from its own infrastructure.
Sandbox
An environment with technical limits on resources such as files, processes or network access. It reduces what execution can affect; protection depends on the actual configured boundaries.
Score
A Jev primitive that scores a case on a configured scale. Its raw value should not automatically be interpreted as a number between 0 and 1.
Share of spend
The percentage of observed spending attributable to a model or task in a view. It combines price and usage; it is not token share, user count or quality.
Skill
A reusable package of instructions and resources for a specialized task. It helps repeat a working method; it does not grant permissions or guarantee a correct result.
SSH
A secure protocol for accessing and running work on another machine or environment, such as WSL or a remote server.
Subagent
An agent delegated part of a task by another agent, with its own context. A separate conversation does not necessarily mean independent machines, files or permissions.
Tailnet
A private Tailscale network that groups authenticated identities and devices. Membership does not replace each service’s own permissions.
Tailscale
A tool that connects users and devices in an identity-based private network, with encrypted traffic and explicit access rules.
Tailscale Funnel
A feature that exposes a local service to the public internet. It should be used with a deliberate public audience, application controls and a defined exposure period.
Tailscale Serve
A feature that shares a local service within the tailnet, subject to its access rules. Useful for previews and internal dashboards.
Tailscale SSH
A feature that manages SSH authentication and authorization on supported nodes through separate policy rules. Allowing TCP 22 on the tailnet is not enough to configure it.
The Dial
Amp’s selector for how much effort a task needs, through the low, medium, high and ultra modes, regardless of which model sits behind it.
Thread
A conversation that retains a task’s context and results. In Amp it can coordinate work in its own environment; a persistent thread does not guarantee that the machine keeps running.
Throughput
Generation speed, usually measured in tokens per second. Compare it under equivalent conditions; it does not by itself include an agent’s tool, turn and retry time.
Token
A unit used by a model to represent and process content. It is not always a word: segmentation depends on the tokenizer. More usage tokens do not mean more users or more completed tasks.
Tool calling
A mechanism through which a model requests a tool execution with arguments. The surrounding system performs it and may require permissions; counting calls does not establish correctness.
TTFT
Time to First Token: elapsed time from sending a request to receiving the first token. It describes the initial wait, not the time to finish the full response or task.
Usage share
The portion of a total attributable to a model or author within a population and period. It can be measured in tokens or requests; the denominator is essential to interpreting the percentage.
WireGuard
The protocol Tailscale uses to encrypt traffic between devices. Encryption is maintained over both direct connections and relays.
Worker
An executing agent assigned a bounded task. It receives scope, permissions and completion criteria, then returns verifiable results to the coordinator.
Workspace
A visible work front that groups tabs, terminals, editors and a task; on its own it is not the same as Git isolation.
Worktree
An additional working directory of a Git repository, usually on another branch. It separates files for parallel tasks but shares repository data and is not a security sandbox.
WSL
Windows Subsystem for Linux: a Linux environment built into Windows where repositories, runtimes and CLI agents can live.