layout	title	nav_order	parent
default	Chapter 1: Getting Started	1	Devika Tutorial

Chapter 1: Getting Started

Welcome to Chapter 1: Getting Started. In this part of Devika Tutorial: Open-Source Autonomous AI Software Engineer, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.

This chapter walks through installing Devika, configuring API keys, and running a first autonomous coding task end-to-end.

Learning Goals

clone and install Devika with all required system dependencies
configure LLM provider credentials and environment variables
launch the Devika web UI and backend services
submit a first task and verify agent output in the workspace

Fast Start Checklist

clone the repository and install Python and Node.js dependencies
install Playwright browsers and Qdrant vector store
set API keys in config.toml for at least one LLM provider
start the backend and frontend servers, then submit a hello-world task

Source References

Summary

You now have a working Devika installation and have executed your first autonomous software engineering task from prompt to generated code.

Next: Chapter 2: Architecture and Agent Pipeline

Depth Expansion Playbook

This chapter is expanded to v1-style depth for production-grade learning and implementation quality.

Strategic Context

tutorial: Devika Tutorial: Open-Source Autonomous AI Software Engineer
tutorial slug: devika-tutorial
chapter focus: Chapter 1: Getting Started
system context: Devika Agentic Software Engineer
objective: move from surface-level usage to repeatable engineering operation

Architecture Decomposition

The Devika stack has three runtime layers: a Python FastAPI backend, a Svelte/Vite frontend, and a Qdrant vector store that persists agent memory across sessions.
The backend entry point is devika.py which starts the FastAPI server; the frontend communicates with it over a REST API on port 1337 by default.
On first launch, Devika initializes the SQLite database for project and session metadata and connects to the Qdrant instance (local or remote) for semantic memory.
Every LLM provider is configured through config.toml; the active model is selected per-project at task submission time.
Playwright is invoked by the browser agent sub-process; it requires Chromium to be installed via playwright install.
The workspace directory (/home/user/projects by default) is where all generated files, git repos, and project artifacts are written.
API key security depends entirely on config.toml permissions; the file must never be committed to version control.
The startup sequence is: Qdrant → FastAPI backend → Vite dev server; all three must be healthy before task submission.

Operator Decision Matrix

Decision Area	Low-Risk Path	High-Control Path	Tradeoff
LLM provider	Claude 3 Haiku for low cost	Claude 3 Opus or GPT-4 for max quality	cost vs output quality
Qdrant mode	local in-memory Qdrant	hosted Qdrant Cloud with persistent storage	simplicity vs durability
Workspace storage	local filesystem	mounted network volume or S3 bucket	portability vs operational overhead
Frontend access	localhost only	reverse proxy with auth	dev convenience vs exposure risk
Python env management	system Python + pip	pyenv + virtualenv + requirements.txt pin	speed vs reproducibility

Failure Modes and Countermeasures

Failure Mode	Early Signal	Root Cause Pattern	Countermeasure
Backend fails to start	port 1337 connection refused	missing Python dependency or wrong Python version	run `pip install -r requirements.txt` and check Python >=3.10
Playwright browser missing	`BrowserType.launch: Executable doesn't exist`	`playwright install` not run	run `playwright install chromium` explicitly
Qdrant not reachable	`Connection refused` on vector store calls	Qdrant not started or wrong port in config	start Qdrant Docker container or set correct host in config.toml
API key invalid	`AuthenticationError` from LLM provider	wrong key or stale key in config.toml	verify key in provider dashboard and re-paste into config.toml
Frontend can't reach backend	API calls return CORS errors	frontend and backend on mismatched ports	ensure VITE_API_BASE_URL matches actual backend port
config.toml not found	`FileNotFoundError` on startup	missing config.toml or wrong working directory	copy `config.example.toml` to `config.toml` before first run

Implementation Runbook

Clone the repository: git clone https://github.com/stitionai/devika.git && cd devika.
Create a Python virtual environment: python -m venv venv && source venv/bin/activate.
Install Python dependencies: pip install -r requirements.txt.
Install Node.js dependencies for the frontend: cd ui && npm install && cd ...
Install Playwright browsers: playwright install chromium.
Copy the example config: cp config.example.toml config.toml and fill in at least one API key.
Start Qdrant: docker run -p 6333:6333 qdrant/qdrant or configure a remote Qdrant URL in config.toml.
Start the backend: python devika.py (confirm the API server is listening on port 1337).
Start the frontend: cd ui && npm run dev and open http://localhost:3000 in a browser; create a project and submit a first task.

Quality Gate Checklist

Python version is 3.10 or higher and the virtualenv is activated
all pip install -r requirements.txt packages resolve without errors
Playwright Chromium browser is installed and playwright install exits cleanly
Qdrant is reachable on the configured port before backend start
config.toml contains at least one valid API key and is not committed to git
backend python devika.py starts without tracebacks and logs "Uvicorn running"
frontend dev server compiles without errors and the UI loads at localhost:3000
first task submission returns agent output in the workspace within expected time

Source Alignment

Cross-Tutorial Connection Map

OpenHands Tutorial — comparable autonomous coding agent with Docker-based setup
SWE-agent Tutorial — alternative CLI-driven autonomous agent
Aider Tutorial — simpler AI coding assistant for quick comparisons
Cline Tutorial — VS Code extension approach to AI coding
Ollama Tutorial — required for local LLM backend with Devika

Advanced Practice Exercises

Install Devika from scratch in a fresh Docker container and document every step that diverges from the README.
Configure two different LLM providers in config.toml and measure cold-start time for each on the same task.
Set up Qdrant Cloud as a remote vector store and verify that agent memory persists across server restarts.
Write a systemd service file or Docker Compose definition that starts the backend, frontend, and Qdrant together.
Submit a task that generates a multi-file Python project and inspect the workspace directory structure produced by the agents.

Review Questions

What is the minimum set of services that must be running before Devika can accept a task?
Where does Devika write generated files, and how is the workspace path configured?
What happens if playwright install is skipped before the first task that requires web research?
Why should config.toml never be committed to git, and how do you prevent it?
How do you verify that the Qdrant vector store is healthy before submitting the first task?

Scenario Playbook 1: Fresh Install on macOS

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: developer clones Devika for the first time on macOS with Homebrew Python
initial hypothesis: Homebrew Python may conflict with virtualenv path resolution
immediate action: verify python3 --version returns >=3.10 before creating the virtualenv
engineering control: use python3 -m venv venv explicitly and activate before any pip commands
verification target: pip list inside venv shows all requirements installed without version conflicts
rollback trigger: any import error in python devika.py startup traceback
communication step: document the exact Python version and venv activation command in team setup notes
learning capture: add macOS-specific install notes to the project wiki and pin the Python version in CI

Scenario Playbook 2: Qdrant Docker Not Started

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: backend starts but every task submission fails immediately with a connection error
initial hypothesis: Qdrant container is not running or is using the wrong port
immediate action: run docker ps to check if the Qdrant container is active
engineering control: add a startup health check script that pings Qdrant on port 6333 before launching the backend
verification target: backend logs show successful Qdrant connection on startup
rollback trigger: health check fails after 10 seconds of waiting
communication step: update the README with a clear "start Qdrant first" step order note
learning capture: encode the Qdrant startup check into the Docker Compose depends_on condition

Scenario Playbook 3: Invalid API Key at Task Submission

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: task is submitted but the agent immediately reports an authentication error
initial hypothesis: the API key in config.toml is either wrong, expired, or for the wrong organization
immediate action: copy the key directly from the provider dashboard and paste into config.toml, then restart the backend
engineering control: add a startup validator that tests LLM provider connectivity with a minimal ping request
verification target: backend startup log shows "LLM provider authenticated" before accepting task requests
rollback trigger: authentication error persists after key replacement
communication step: check provider dashboard for key status, quota limits, and billing validity
learning capture: document key rotation procedure and add config.toml validation to the pre-launch checklist

Scenario Playbook 4: Frontend Cannot Reach Backend

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: UI loads but all task submissions return network errors in the browser console
initial hypothesis: VITE_API_BASE_URL environment variable is set to a different port than the backend
immediate action: check ui/.env or ui/vite.config.js for the API base URL and compare with the actual backend port
engineering control: standardize backend port to 1337 and frontend proxy setting to match; document in .env.example
verification target: browser network tab shows successful POST to /api/execute-agent with 200 response
rollback trigger: CORS errors persist after port correction
communication step: add port configuration to the onboarding checklist for new contributors
learning capture: create a single .env file at the repo root that sources both backend and frontend port settings

Scenario Playbook 5: Playwright Browser Crash on Task

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: research-heavy task fails mid-run with a Playwright executable not found error
initial hypothesis: Playwright was installed in a different virtualenv or system Python than the one running the backend
immediate action: activate the correct virtualenv and run playwright install chromium inside it
engineering control: add playwright install chromium as a post-install step in the project Makefile or setup script
verification target: playwright --version resolves without error inside the active virtualenv
rollback trigger: Playwright crashes even after reinstall; check for sandboxing issues in CI or Docker environments
communication step: update the CI pipeline YAML to include playwright install chromium as a named setup step
learning capture: add explicit Playwright version pinning in requirements.txt to prevent future environment drift

What Problem Does This Solve?

Devika's installation complexity stems from having three distinct runtimes (Python backend, Node.js frontend, Qdrant vector store) that must all be healthy simultaneously before the agent pipeline can function. Without a structured setup sequence, engineers frequently encounter cascading failures where one missing dependency causes misleading errors in a different layer. This chapter establishes the correct startup order, dependency inventory, and verification checkpoints so that every team member reaches a working baseline without guesswork.

How it Works Under the Hood

The Python backend reads config.toml on startup and initializes provider clients for every API key present.
FastAPI registers all agent API endpoints and starts Uvicorn on port 1337.
The Qdrant client connects to the configured vector store URL and verifies the collection schema for agent memory.
The SQLite database is initialized (or reopened) for project, session, and task metadata storage.
When the frontend submits a task, FastAPI dispatches it to the agent orchestrator which spawns the multi-agent pipeline.
Generated files are written to the workspace path defined in config.toml under the project name subdirectory.

Source Walkthrough

Devika README Getting Started — Why it matters: the canonical install sequence that defines the correct dependency order.
Devika config.example.toml — Why it matters: the authoritative reference for all configuration keys and their default values.
Devika requirements.txt — Why it matters: the pinned Python dependency list that determines runtime compatibility.
Devika devika.py — Why it matters: the backend entry point that wires all services together on startup.

Chapter Connections

Scenario Playbook 1: Chapter 1: Getting Started

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: incoming request volume spikes after release
initial hypothesis: identify the smallest reproducible failure boundary
immediate action: protect user-facing stability before optimization work
engineering control: introduce adaptive concurrency limits and queue bounds
verification target: latency p95 and p99 stay within defined SLO windows
rollback trigger: pre-defined quality gate fails for two consecutive checks
communication step: publish incident status with owner and ETA
learning capture: add postmortem and convert findings into automated tests

Scenario Playbook 2: Chapter 1: Getting Started

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: tool dependency latency increases under concurrency
initial hypothesis: identify the smallest reproducible failure boundary
immediate action: protect user-facing stability before optimization work
engineering control: enable staged retries with jitter and circuit breaker fallback
verification target: error budget burn rate remains below escalation threshold
rollback trigger: pre-defined quality gate fails for two consecutive checks
communication step: publish incident status with owner and ETA
learning capture: add postmortem and convert findings into automated tests

Scenario Playbook 3: Chapter 1: Getting Started

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: schema updates introduce incompatible payloads
initial hypothesis: identify the smallest reproducible failure boundary
immediate action: protect user-facing stability before optimization work
engineering control: pin schema versions and add compatibility shims
verification target: throughput remains stable under target concurrency
rollback trigger: pre-defined quality gate fails for two consecutive checks
communication step: publish incident status with owner and ETA
learning capture: add postmortem and convert findings into automated tests

Scenario Playbook 4: Chapter 1: Getting Started

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: environment parity drifts between staging and production
initial hypothesis: identify the smallest reproducible failure boundary
immediate action: protect user-facing stability before optimization work
engineering control: restore environment parity via immutable config promotion
verification target: retry volume stays bounded without feedback loops
rollback trigger: pre-defined quality gate fails for two consecutive checks
communication step: publish incident status with owner and ETA
learning capture: add postmortem and convert findings into automated tests

Scenario Playbook 5: Chapter 1: Getting Started

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: access policy changes reduce successful execution rates
initial hypothesis: identify the smallest reproducible failure boundary
immediate action: protect user-facing stability before optimization work
engineering control: re-scope credentials and rotate leaked or stale keys
verification target: data integrity checks pass across write/read cycles
rollback trigger: pre-defined quality gate fails for two consecutive checks
communication step: publish incident status with owner and ETA
learning capture: add postmortem and convert findings into automated tests

Scenario Playbook 6: Chapter 1: Getting Started

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: background jobs accumulate and exceed processing windows
initial hypothesis: identify the smallest reproducible failure boundary
immediate action: protect user-facing stability before optimization work
engineering control: activate degradation mode to preserve core user paths
verification target: audit logs capture all control-plane mutations
rollback trigger: pre-defined quality gate fails for two consecutive checks
communication step: publish incident status with owner and ETA
learning capture: add postmortem and convert findings into automated tests

Scenario Playbook 7: Chapter 1: Getting Started

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: incoming request volume spikes after release
initial hypothesis: identify the smallest reproducible failure boundary
immediate action: protect user-facing stability before optimization work
engineering control: introduce adaptive concurrency limits and queue bounds
verification target: latency p95 and p99 stay within defined SLO windows
rollback trigger: pre-defined quality gate fails for two consecutive checks
communication step: publish incident status with owner and ETA
learning capture: add postmortem and convert findings into automated tests

Scenario Playbook 8: Chapter 1: Getting Started

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: tool dependency latency increases under concurrency
initial hypothesis: identify the smallest reproducible failure boundary
immediate action: protect user-facing stability before optimization work
engineering control: enable staged retries with jitter and circuit breaker fallback
verification target: error budget burn rate remains below escalation threshold
rollback trigger: pre-defined quality gate fails for two consecutive checks
communication step: publish incident status with owner and ETA
learning capture: add postmortem and convert findings into automated tests

Scenario Playbook 9: Chapter 1: Getting Started

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: schema updates introduce incompatible payloads
initial hypothesis: identify the smallest reproducible failure boundary
immediate action: protect user-facing stability before optimization work
engineering control: pin schema versions and add compatibility shims
verification target: throughput remains stable under target concurrency
rollback trigger: pre-defined quality gate fails for two consecutive checks
communication step: publish incident status with owner and ETA
learning capture: add postmortem and convert findings into automated tests

Scenario Playbook 10: Chapter 1: Getting Started

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: environment parity drifts between staging and production
initial hypothesis: identify the smallest reproducible failure boundary
immediate action: protect user-facing stability before optimization work
engineering control: restore environment parity via immutable config promotion
verification target: retry volume stays bounded without feedback loops
rollback trigger: pre-defined quality gate fails for two consecutive checks
communication step: publish incident status with owner and ETA
learning capture: add postmortem and convert findings into automated tests

Scenario Playbook 11: Chapter 1: Getting Started

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: access policy changes reduce successful execution rates
initial hypothesis: identify the smallest reproducible failure boundary
immediate action: protect user-facing stability before optimization work
engineering control: re-scope credentials and rotate leaked or stale keys
verification target: data integrity checks pass across write/read cycles
rollback trigger: pre-defined quality gate fails for two consecutive checks
communication step: publish incident status with owner and ETA
learning capture: add postmortem and convert findings into automated tests

Scenario Playbook 12: Chapter 1: Getting Started

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: background jobs accumulate and exceed processing windows
initial hypothesis: identify the smallest reproducible failure boundary
immediate action: protect user-facing stability before optimization work
engineering control: activate degradation mode to preserve core user paths
verification target: audit logs capture all control-plane mutations
rollback trigger: pre-defined quality gate fails for two consecutive checks
communication step: publish incident status with owner and ETA
learning capture: add postmortem and convert findings into automated tests

Scenario Playbook 13: Chapter 1: Getting Started

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: incoming request volume spikes after release
initial hypothesis: identify the smallest reproducible failure boundary
immediate action: protect user-facing stability before optimization work
engineering control: introduce adaptive concurrency limits and queue bounds
verification target: latency p95 and p99 stay within defined SLO windows
rollback trigger: pre-defined quality gate fails for two consecutive checks
communication step: publish incident status with owner and ETA
learning capture: add postmortem and convert findings into automated tests

Scenario Playbook 14: Chapter 1: Getting Started

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: tool dependency latency increases under concurrency
initial hypothesis: identify the smallest reproducible failure boundary
immediate action: protect user-facing stability before optimization work
engineering control: enable staged retries with jitter and circuit breaker fallback
verification target: error budget burn rate remains below escalation threshold
rollback trigger: pre-defined quality gate fails for two consecutive checks
communication step: publish incident status with owner and ETA
learning capture: add postmortem and convert findings into automated tests

Scenario Playbook 15: Chapter 1: Getting Started

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: schema updates introduce incompatible payloads
initial hypothesis: identify the smallest reproducible failure boundary
immediate action: protect user-facing stability before optimization work
engineering control: pin schema versions and add compatibility shims
verification target: throughput remains stable under target concurrency
rollback trigger: pre-defined quality gate fails for two consecutive checks
communication step: publish incident status with owner and ETA
learning capture: add postmortem and convert findings into automated tests

Scenario Playbook 16: Chapter 1: Getting Started

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: environment parity drifts between staging and production
initial hypothesis: identify the smallest reproducible failure boundary
immediate action: protect user-facing stability before optimization work
engineering control: restore environment parity via immutable config promotion
verification target: retry volume stays bounded without feedback loops
rollback trigger: pre-defined quality gate fails for two consecutive checks
communication step: publish incident status with owner and ETA
learning capture: add postmortem and convert findings into automated tests

Scenario Playbook 17: Chapter 1: Getting Started

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: access policy changes reduce successful execution rates
initial hypothesis: identify the smallest reproducible failure boundary
immediate action: protect user-facing stability before optimization work
engineering control: re-scope credentials and rotate leaked or stale keys
verification target: data integrity checks pass across write/read cycles
rollback trigger: pre-defined quality gate fails for two consecutive checks
communication step: publish incident status with owner and ETA
learning capture: add postmortem and convert findings into automated tests

Scenario Playbook 18: Chapter 1: Getting Started

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: background jobs accumulate and exceed processing windows
initial hypothesis: identify the smallest reproducible failure boundary
immediate action: protect user-facing stability before optimization work
engineering control: activate degradation mode to preserve core user paths
verification target: audit logs capture all control-plane mutations
rollback trigger: pre-defined quality gate fails for two consecutive checks
communication step: publish incident status with owner and ETA
learning capture: add postmortem and convert findings into automated tests

Scenario Playbook 19: Chapter 1: Getting Started

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: incoming request volume spikes after release
initial hypothesis: identify the smallest reproducible failure boundary
immediate action: protect user-facing stability before optimization work
engineering control: introduce adaptive concurrency limits and queue bounds
verification target: latency p95 and p99 stay within defined SLO windows
rollback trigger: pre-defined quality gate fails for two consecutive checks
communication step: publish incident status with owner and ETA
learning capture: add postmortem and convert findings into automated tests

Scenario Playbook 20: Chapter 1: Getting Started

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: tool dependency latency increases under concurrency
initial hypothesis: identify the smallest reproducible failure boundary
immediate action: protect user-facing stability before optimization work
engineering control: enable staged retries with jitter and circuit breaker fallback
verification target: error budget burn rate remains below escalation threshold
rollback trigger: pre-defined quality gate fails for two consecutive checks
communication step: publish incident status with owner and ETA
learning capture: add postmortem and convert findings into automated tests

Scenario Playbook 21: Chapter 1: Getting Started

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: schema updates introduce incompatible payloads
initial hypothesis: identify the smallest reproducible failure boundary
immediate action: protect user-facing stability before optimization work
engineering control: pin schema versions and add compatibility shims
verification target: throughput remains stable under target concurrency
rollback trigger: pre-defined quality gate fails for two consecutive checks
communication step: publish incident status with owner and ETA
learning capture: add postmortem and convert findings into automated tests

Scenario Playbook 22: Chapter 1: Getting Started

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: environment parity drifts between staging and production
initial hypothesis: identify the smallest reproducible failure boundary
immediate action: protect user-facing stability before optimization work
engineering control: restore environment parity via immutable config promotion
verification target: retry volume stays bounded without feedback loops
rollback trigger: pre-defined quality gate fails for two consecutive checks
communication step: publish incident status with owner and ETA
learning capture: add postmortem and convert findings into automated tests

Scenario Playbook 23: Chapter 1: Getting Started

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: access policy changes reduce successful execution rates
initial hypothesis: identify the smallest reproducible failure boundary
immediate action: protect user-facing stability before optimization work
engineering control: re-scope credentials and rotate leaked or stale keys
verification target: data integrity checks pass across write/read cycles
rollback trigger: pre-defined quality gate fails for two consecutive checks
communication step: publish incident status with owner and ETA
learning capture: add postmortem and convert findings into automated tests

Scenario Playbook 24: Chapter 1: Getting Started

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: background jobs accumulate and exceed processing windows
initial hypothesis: identify the smallest reproducible failure boundary
immediate action: protect user-facing stability before optimization work
engineering control: activate degradation mode to preserve core user paths
verification target: audit logs capture all control-plane mutations
rollback trigger: pre-defined quality gate fails for two consecutive checks
communication step: publish incident status with owner and ETA
learning capture: add postmortem and convert findings into automated tests

Scenario Playbook 25: Chapter 1: Getting Started

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: incoming request volume spikes after release
initial hypothesis: identify the smallest reproducible failure boundary
immediate action: protect user-facing stability before optimization work
engineering control: introduce adaptive concurrency limits and queue bounds
verification target: latency p95 and p99 stay within defined SLO windows
rollback trigger: pre-defined quality gate fails for two consecutive checks
communication step: publish incident status with owner and ETA
learning capture: add postmortem and convert findings into automated tests

Scenario Playbook 26: Chapter 1: Getting Started

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: tool dependency latency increases under concurrency
initial hypothesis: identify the smallest reproducible failure boundary
immediate action: protect user-facing stability before optimization work
engineering control: enable staged retries with jitter and circuit breaker fallback
verification target: error budget burn rate remains below escalation threshold
rollback trigger: pre-defined quality gate fails for two consecutive checks
communication step: publish incident status with owner and ETA
learning capture: add postmortem and convert findings into automated tests

Scenario Playbook 27: Chapter 1: Getting Started

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: schema updates introduce incompatible payloads
initial hypothesis: identify the smallest reproducible failure boundary
immediate action: protect user-facing stability before optimization work
engineering control: pin schema versions and add compatibility shims
verification target: throughput remains stable under target concurrency
rollback trigger: pre-defined quality gate fails for two consecutive checks
communication step: publish incident status with owner and ETA
learning capture: add postmortem and convert findings into automated tests

Scenario Playbook 28: Chapter 1: Getting Started

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: environment parity drifts between staging and production
initial hypothesis: identify the smallest reproducible failure boundary
immediate action: protect user-facing stability before optimization work
engineering control: restore environment parity via immutable config promotion
verification target: retry volume stays bounded without feedback loops
rollback trigger: pre-defined quality gate fails for two consecutive checks
communication step: publish incident status with owner and ETA
learning capture: add postmortem and convert findings into automated tests

Scenario Playbook 29: Chapter 1: Getting Started

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: access policy changes reduce successful execution rates
initial hypothesis: identify the smallest reproducible failure boundary
immediate action: protect user-facing stability before optimization work
engineering control: re-scope credentials and rotate leaked or stale keys
verification target: data integrity checks pass across write/read cycles
rollback trigger: pre-defined quality gate fails for two consecutive checks
communication step: publish incident status with owner and ETA
learning capture: add postmortem and convert findings into automated tests

Scenario Playbook 30: Chapter 1: Getting Started

tutorial context: Devika Tutorial: Open-Source Autonomous AI Software Engineer
trigger condition: background jobs accumulate and exceed processing windows
initial hypothesis: identify the smallest reproducible failure boundary
immediate action: protect user-facing stability before optimization work
engineering control: activate degradation mode to preserve core user paths
verification target: audit logs capture all control-plane mutations
rollback trigger: pre-defined quality gate fails for two consecutive checks
communication step: publish incident status with owner and ETA
learning capture: add postmortem and convert findings into automated tests

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