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sre-agent

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SRE Agent — An AI-powered MCP server for production incident triage. Takes natural-language symptom reports, plans structured investigations using Gemini, executes parallel workers (logs, metrics, deploys, runbooks), synthesizes root-cause reports, and proposes remediation patches with human approval gates.

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# SRE Agent MCP Server

![Python](https://img.shields.io/badge/python-3.14+-blue.svg)
![License](https://img.shields.io/badge/license-MIT-green.svg)
![FastMCP](https://img.shields.io/badge/FastMCP-enabled-orange.svg)
![uv](https://img.shields.io/badge/uv-dependency%20management-purple.svg)

## Table of Contents

- [The Problem](#the-problem)
- [What SRE Agent Does](#what-sre-agent-does)
- [Demo](#demo)
- [Architecture & System Design](#architecture--system-design)
- [Project Structure](#project-structure)
- [Workers](#workers)
- [MCP Interface](#mcp-interface)
- [Dataset Scenarios](#dataset-scenarios)
- [Setup Instructions](#setup-instructions)
- [Environment Variables](#environment-variables)
- [Running the Server](#running-the-server)
- [Connecting from an MCP Client](#connecting-from-an-mcp-client)
- [Example Queries](#example-queries)
- [Testing](#testing)
- [Troubleshooting](#troubleshooting)
- [Mandatory Features Implemented](#mandatory-features-implemented)
- [Custom Features Implemented](#custom-features-implemented)
- [Contributing](#contributing)
- [License](#license)

---

## The Problem

Production incident response is one of the most high-pressure, time-sensitive activities in software engineering. When a service goes down at 3 AM, an on-call Site Reliability Engineer (SRE) is paged and must answer a cascade of questions under extreme time pressure:

- **What is actually broken?** Is it a single endpoint, an entire service, or a cascading failure across multiple systems?
- **What changed?** Was there a recent deployment, a configuration change, or an infrastructure event that correlates with the start of the incident?
- **Where is the evidence?** Logs, metrics, and traces are scattered across different observability platforms. The engineer must query each one individually, mentally correlate timestamps, and piece together a timeline.
- **What does the runbook say?** Most teams maintain runbooks for known failure modes, but finding the right runbook and following its steps while simultaneously investigating is cognitively demanding.
- **What is the fix?** Once the root cause is identified, the engineer must decide on remediation: rollback a deployment, scale infrastructure, toggle a feature flag, or apply a hotfix. Each option carries risk.

This process is slow, error-prone, and mentally exhausting. Studies show that Mean Time to Resolution (MTTR) for production incidents averages 1-4 hours across the industry, with a significant portion of that time spent on the investigation phase rather than the actual fix. During an outage, every minute costs money: lost revenue, SLA violations, customer churn, and engineering productivity drain.

The core challenge is that **incident triage is fundamentally a multi-step reasoning task** that requires gathering evidence from multiple sources, forming hypotheses, testing them against data, and arriving at a root cause. Today, this reasoning happens entirely in the engineer's head, with no structured framework to guide the investigation or prevent cognitive shortcuts that lead to wrong conclusions.

### Why Existing Tools Fall Short

Current incident response tooling addresses individual pieces of the puzzle but not the investigation workflow itself:

- **Observability platforms** (Datadog, Grafana, New Relic) are excellent at storing and visualizing data, but they don't reason about what the data means or guide investigation.
- **Alerting systems** (PagerDuty, OpsGenie) notify the right person, but provide no investigation support beyond the initial alert context.
- **Runbook tools** (Confluence, Notion) store institutional knowledge, but there's no automated way to match symptoms to the right runbook or verify that its steps are being followed.
- **ChatOps bots** (custom Slack bots) can query individual data sources, but lack the ability to plan a structured investigation or synthesize findings across sources.

What's missing is an **intelligent orchestration layer** that can take a natural-language description of symptoms, plan a structured investigation, execute it by querying the right tools in the right order, and synthesize the results into an actionable incident report with remediation proposals.

## What SRE Agent Does

SRE Agent is a domain-specific MCP server that acts as an **AI-powered first responder** for production incidents. It bridges the gap between raw observability data and actionable incident response by automating the investigation workflow that SREs currently perform manually.

When an on-call engineer reports symptoms (e.g. *"Prod checkout is spiking 500s since 14:05 UTC"*), the server:

1. **Plans the investigation** &mdash; An LLM-powered orchestrator analyzes the symptoms, generates ranked hypotheses (e.g. "bad deployment", "dependency failure", "resource exhaustion"), and produces a structured triage plan with phased tasks and dependency ordering.

2. **Executes the investigation in parallel** &mdash; Specialized workers query logs, metrics, deployment records, and runbooks simultaneously. Same-phase tasks run in parallel for speed; cross-phase dependencies are enforced automatically.

3. **Synthesizes findings into an incident report** &mdash; Worker outputs are aggregated and analyzed to produce a root-cause assessment, evidence summary, severity classification, and recommended next actions.

4. **Proposes remediation with human approval** &mdash; The system generates patch artifacts (e.g. rollback diffs, runbook updates) but never applies them automatically. A human approval gate ensures the engineer reviews and explicitly approves any changes before they take effect.

The result: an engineer who would normally spend 30-60 minutes querying dashboards, reading logs, and cross-referencing deployment history can get a structured incident report with root-cause analysis and remediation options in under a minute.

### Use Cases

- **On-call triage acceleration** &mdash; Reduce MTTR by automating evidence gathering and correlation during production incidents.
- **Incident postmortem preparation** &mdash; Generate structured timelines and evidence summaries that feed directly into postmortem documents.
- **Runbook validation** &mdash; Automatically match incident symptoms to relevant runbooks and verify that remediation steps are applicable.
- **Junior SRE training** &mdash; The structured triage plan serves as an educational tool, showing less experienced engineers how to systematically investigate an outage.
- **AI-assisted pair debugging** &mdash; Use the MCP client interactively to explore an incident step by step, with the agent providing context and suggestions at each stage.

---

## Demo

[demo_sre_agent.mp4](docs/demo/demo_sre_agent.mp4)

---

## Architecture & System Design

The system follows Clean Architecture with an explicit composition root, ensuring that business logic is decoupled from infrastructure concerns and the MCP protocol boundary.

![sre_design.svg](https://raw.githubusercontent.com/benmoggee/sre-agent/main/docs/images/sre_design.svg)

### Layering

| Layer | Path | Responsibility |
|-------|------|----------------|
| **Entities** | `mcp_server/src/entities/` | Pure domain models (Pydantic). No external dependencies. |
| **Use Cases** | `mcp_server/src/app/` | Orchestration, phase execution, synthesis, worker dispatch. Depends only on port interfaces (`ports.py`). |
| **Infrastructure** | `mcp_server/src/infrastructure/` | Adapter implementations: Gemini LLM gateway, SRE tool gateway, approval store. |
| **MCP Routers** | `mcp_server/src/routers/` | Protocol boundary. Registers tools, resources, and prompts with FastMCP. |
| **Composition Root** | `mcp_server/src/workflows/main_workflow.py` | Wires infrastructure adapters into use cases and drives the end-to-end flow. |

### Ports and Adapters

Dependency injection interfaces are defined in `mcp_server/src/app/ports.py`:

- **`LanguageModelPort`** &mdash; `generate_json()` and `generate_text()` for LLM calls
- **`SreToolGatewayPort`** &mdash; logs, metrics, deploys, runbooks, and patch operations
- **`ApprovalStorePort`** &mdash; create, approve, and apply patch proposals

Concrete adapters in `mcp_server/src/infrastructure/gateways.py`:

- `GeminiLanguageModelGateway` &mdash; Google Gemini with structured output
- `SreGuardianToolGateway` &mdash; backed by local `mcp_tools/sre_guardian`
- `PatchApprovalStoreGateway` &mdash; in-memory approval store

### Workflow Phases

1. **Start** &mdash; Accepts user query and initializes progress tracking.
2. **Orchestrator** &mdash; Builds an `IncidentTriagePlan` with hypotheses, tasks, dependencies, and phase numbers. Uses Gemini structured output or deterministic fallback.
3. **Worker Execution** &mdash; Groups tasks by phase. Runs same-phase tasks in parallel via `asyncio.gather()`. Enforces `depends_on` gates before downstream tasks.
4. **Synthesis** &mdash; Aggregates worker outputs into a structured incident report with root-cause assessment, evidence, and next actions. Creates a patch proposal.
5. **Approval Gate** &mdash; Patch proposals remain pending until explicit human approval. If approved, the patch is applied; otherwise it stays unapplied.
6. **Completion** &mdash; Returns final structured payload including plan, per-task results, synthesis output, and approval state.

---

## Project Structure

```text
.
├── mcp_server/
│   └── src/
│       ├── server.py                  # FastMCP entry point
│       ├── routers/                   # MCP tool/resource/prompt registration
│       │   ├── tools.py
│       │   ├── resources.py
│       │   └── prompts.py
│       ├── app/                       # Use-case logic
│       │   ├── ports.py               # Dependency injection interfaces
│       │   ├── orchestrator.py        # Query -> IncidentTriagePlan
│       │   ├── phase_runner.py        # Phased parallel task execution
│       │   ├── task_runner.py         # Single task dispatch
│       │   ├── synthesizer.py         # Findings -> report + patch
│       │   └── worker_registry.py     # Tool name -> worker mapping
│       ├── infrastructure/
│       │   └── gateways.py            # Gemini, SRE tool, approval adapters
│       ├── entities/                  # Pydantic domain models
│       │   ├── incident_triage_plan.py
│       │   ├── tasks.py
│       │   ├── workers.py
│       │   ├── hypothesis.py
│       │   ├── severity.py
│       │   └── ...
│       ├── nodes/                     # Concrete worker implementations
│       │   ├── base.py                # BaseWorker abstract class
│       │   ├── logs_query_worker.py
│       │   ├── metrics_query_worker.py
│       │   ├── deploy_list_worker.py
│       │   ├── incident_context_worker.py
│       │   ├── runbooks_search_worker.py
│       │   ├── runbook_get_worker.py
│       │   └── patch_generate_worker.py
│       ├── tools/                     # Tool implementation layer
│       ├── workflows/
│       │   └── main_workflow.py       # Composition root
│       ├── mcp_tools/
│       │   └── sre_guardian.py        # Local fallback tool backends
│       ├── prompts/                   # LLM prompt templates
│       ├── resources/                 # MCP resource implementations
│       ├── config/
│       │   └── settings.py            # Environment-based config (Pydantic)
│       ├── models/
│       │   └── get_model.py           # Gemini client initialization
│       └── utils/
│           ├── approval_store.py      # In-memory patch proposals
│           ├── opik_utils.py          # Observability integration
│           └── ...
├── mcp_client/
│   └── src/
│       ├── client.py                  # Interactive MCP client
│       ├── settings.py                # Client configuration
│       └── utils/                     # Agent loop, LLM, parsing, commands
├── data/scenarios/                    # Sample incident data
│   ├── bad_deploy/
│   ├── dependency_latency/
│   └── saturation_db_pool/
├── docs/
├── pyproject.toml
├── .python-version
├── .env.sample
└── uv.lock
```

---

## Workers

All workers inherit from `BaseWorker` and implement `async run(task, context_id) -> WorkerResult`.

| Worker | Tool | Purpose |
|--------|------|---------|
| `LogsQueryWorker` | `logs_query` | Query structured logs by service, level, and time range |
| `MetricsQueryWorker` | `metrics_query` | Fetch time-series metrics (e.g. `http_5xx_rate`) |
| `DeploysListWorker` | `deploys_list` | List recent deployments in the incident window |
| `IncidentContextWorker` | `incident_get_context` | Retrieve incident context and environment info |
| `RunbooksSearchWorker` | `runbooks_search` | Search runbooks by symptom or keyword |
| `RunbookGetWorker` | `runbooks_get` | Fetch full runbook content by ID |
| `PatchGenerateWorker` | `patch_generate` | Generate rollback/remediation diffs |

---

## MCP Interface

### Tools

Registered in `mcp_server/src/routers/tools.py`:

- **Context management:** `incident_start_context`, `incident_get_context`
- **Observability:** `logs_query`, `metrics_query`
- **Deployment:** `deploys_list`
- **Runbooks:** `runbooks_search`, `runbooks_get`
- **Patching:** `patch_generate`
- **Approval:** `request_patch_approval`, `set_patch_approval`, `apply_approved_patch`, `get_patch_approval_status`
- **Workflow:** `process_user_query_workflow` (main orchestrator entry point)

### Resources

- `system://status` &mdash; System health (CPU, uptime)
- `system://memory` &mdash; Memory usage stats

### Prompts

- `sre_triage_execution_prompt` &mdash; Full SRE instructions for incident triage. This prompt ties together the investigation tools into a guided agentic workflow and includes steps where user feedback and confirmation are required (e.g. reviewing the triage plan, approving patch proposals before application).

---

## Dataset Scenarios

Sample incident datasets in `data/scenarios/` for local fallback and demonstration:

| Scenario | Description |
|----------|-------------|
| `bad_deploy` | A code deployment introduces a connection pool exhaustion bug, causing 500 errors |
| `dependency_latency` | A downstream service dependency starts timing out, cascading failures upstream |
| `saturation_db_pool` | Database connection pool saturation under increased load |

Each scenario includes `logs.jsonl`, `metrics.json`, `deploys.json`, and `runbooks.md`. These are consumed by `mcp_server/src/mcp_tools/sre_guardian.py` when local fallback is enabled.

---

## Setup Instructions

### Prerequisites

- Python 3.14+
- [uv](https://docs.astral.sh/uv/) (fast Python package manager)

### Installation

1. Clone the repository:
```bash
git clone https://github.com/your-username/sre-agent.git
cd sre-agent
```

2. Install dependencies:
```bash
uv sync
```

3. Create your environment file from the sample:
```bash
cp .env.sample .env
```

4. Fill in `.env` with your real keys (see [Environment Variables](#environment-variables) below).

---

## Environment Variables

A sample file is provided at `.env.sample`. Copy it to `.env` and replace the placeholder values.

| Variable | Required | Default | Description |
|----------|----------|---------|-------------|
| `GOOGLE_API_KEY` | **Yes** | &mdash; | Gemini API key. Get it from [Google AI Studio](https://aistudio.google.com/app/apikey). |
| `MODEL_ID` | No | `gemini-2.5-flash` | Gemini model ID to use for orchestration and synthesis. |
| `ORCHESTRATOR_ENABLE_OFFLINE_FALLBACK` | No | `true` | Use deterministic task plan when LLM is unavailable. |
| `MCP_ENABLE_LOCAL_FALLBACK` | No | `true` | Use local scenario data when MCP backend is unavailable. |
| `SRE_SAMPLE_SCENARIO` | No | `bad_deploy` | Which scenario to load (`bad_deploy`, `dependency_latency`, `saturation_db_pool`). |
| `LOG_LEVEL` | No | `20` | Logging level (10=DEBUG, 20=INFO, 30=WARNING). |
| `LOG_LEVEL_DEPENDENCIES` | No | `30` | Logging level for third-party libraries. |
| `OPIK_API_KEY` | No | &mdash; | [Opik](https://www.comet.com/opik) API key for observability. |
| `OPIK_WORKSPACE` | No | &mdash; | Opik workspace name. |
| `OPIK_PROJECT_NAME` | No | `sre_guardian` | Opik project name. |
| `OPENAI_API_KEY` | No | &mdash; | OpenAI API key (reserved for future provider support). |

---

## Running the Server

### MCP Server (stdio)

```bash
uv --directory mcp_server run -m src.server --transport stdio
```

### MCP Server (streamable HTTP)

```bash
uv --directory mcp_server run -m src.server --transport streamable-http --port 8001
```

### Interactive MCP Client

In-memory server mode (MCP server runs in the same process):
```bash
uv run python -m mcp_client.src.client
```

Stdio server mode (connects to external MCP server):
```bash
uv run python -m mcp_client.src.client --transport stdio
```

---

## Connecting from an MCP Client

### Cursor / Generic MCP Client

```json
{
  "mcp_servers": {
    "sre-first-responder": {
      "command": "uv",
      "args": [
        "--directory",
        "/absolute/path/to/sre-agent/mcp_server",
        "run",
        "-m",
        "src.server",
        "--transport",
        "stdio"
      ],
      "env": {
        "GOOGLE_API_KEY": "your-google-api-key",
        "OPIK_API_KEY": "your-opik-api-key",
        "OPIK_PROJECT_NAME": "sre_guardian"
      }
    }
  }
}
```

### Claude Desktop

A working example is included in `claude_desktop_config.json`.

---

## Example Queries

```
Prod checkout is spiking 500s since 14:05 UTC. Start triage for checkout-api.
Start triage for checkout-api and check if a recent deploy caused regressions.
Investigate checkout latency and timeout errors in production.
```

---

## Testing

Compile check:
```bash
python -m compileall mcp_server/src mcp_client/src
```

Workflow smoke test:
```bash
python - <<'PY'
import asyncio
from mcp_server.src.workflows.main_workflow import process_user_query

async def main():
    result = await process_user_query(
        user_query='Prod checkout is spiking 500s since 14:05 UTC. Start triage for checkout-api.',
        render_output=False,
    )
    print(result['status'], result['task_count'], result['result_count'])

asyncio.run(main())
PY
```

---

## Troubleshooting

**`ModuleNotFoundError: No module named 'mcp_server'`**
Use module-based invocation:
```bash
uv --directory mcp_server run -m src.server --transport stdio
```

**Gemini quota errors (`429 RESOURCE_EXHAUSTED`)**
This is Gemini rate limiting. Wait for the quota to reset or enable offline fallback by setting `ORCHESTRATOR_ENABLE_OFFLINE_FALLBACK=true` in your `.env`.

**Opik logs to `Default Project`**
Set `OPIK_PROJECT_NAME` in your `.env` file and ensure Opik is configured in the process creating spans.

---

## Mandatory Features Implemented

1. **MCP server in Python using FastMCP**
   - Implemented in `mcp_server/src/server.py`. The server is created with FastMCP and supports both `stdio` and `streamable-http` transports. All tools, resources, and prompts are registered via the routers layer in `mcp_server/src/routers/`.

2. **At least one MCP tool with meaningful functionality**
   - Multiple MCP tools are implemented and registered in `mcp_server/src/routers/tools.py`, including `logs_query`, `metrics_query`, `deploys_list`, `runbooks_search`, `runbooks_get`, `patch_generate`, `incident_start_context`, `incident_get_context`, approval workflow tools (`request_patch_approval`, `set_patch_approval`, `apply_approved_patch`, `get_patch_approval_status`), and the main `process_user_query_workflow` orchestrator entry point.

3. **At least one MCP prompt with user feedback workflow**
   - The `sre_triage_execution_prompt` is implemented in `mcp_server/src/prompts/sre_instructions_prompt.py` and registered in `mcp_server/src/routers/prompts.py`. This prompt drives a full agentic triage workflow and includes explicit user feedback steps: the human approval gate requires the engineer to review the generated patch proposal and explicitly approve or reject it before any changes are applied (via `request_patch_approval` -> `set_patch_approval` -> `apply_approved_patch`).

4. **Published on a public GitHub repository**
   - The complete project is hosted on GitHub.

5. **Initialized using `uv` for dependency management**
   - The project uses `uv` with `pyproject.toml`, `.python-version` (3.14.0), and `uv.lock`. Dependencies are installed via `uv sync`.

6. **Organized source structure**
   - Code is organized under `mcp_server/src/` and `mcp_client/src/` with clear separation: `server.py` (entry point), `routers/` (MCP registration), `tools/` (tool implementations), `app/` (business logic), `entities/` (domain models), `infrastructure/` (adapters), `config/settings.py` (Pydantic-based configuration), `prompts/` (prompt templates), `resources/` (resource implementations), and `utils/` (utilities).

7. **Comprehensive README.md**
   - This README includes: project description, use cases, architecture diagram, setup instructions, environment variable documentation, MCP client configuration JSON, mandatory and custom feature sections, and troubleshooting guide.

8. **No API keys or sensitive credentials committed**
   - A `.env.sample` file with placeholder values is provided. The `.env` file is gitignored. The README explains which keys are needed and how to obtain them.

---

## Custom Features Implemented

1. **MCP client with custom orchestration/planning** *(Client & Integration)*
   - `mcp_client/src/client.py` implements a full interactive MCP client with a custom agent loop (`handle_agent_loop_utils.py`), tool-call orchestration, thinking mode toggle, transport mode selection (in-memory or stdio), and conversation history management. This demonstrates programmatic usage of the MCP server beyond Cursor or Claude Desktop.

2. **MCP resources for contextual information** *(Client & Integration)*
   - Two MCP resources are implemented in `mcp_server/src/resources/`: `system://status` provides real-time system health data (CPU, uptime) and `system://memory` provides memory usage statistics. These are registered in `mcp_server/src/routers/resources.py` and give the agent contextual awareness of the environment it's running in.

3. **Multiple workflow patterns (sequential, parallel, conditional)** *(Workflow & Agentic Patterns)*
   - The phase runner (`mcp_server/src/app/phase_runner.py`) implements three distinct patterns: **sequential** phase ordering (phase 1 completes before phase 2 begins), **parallel** execution within each phase (all same-phase tasks run concurrently via `asyncio.gather()`), and **conditional branching** through dependency gates (`depends_on` fields that skip downstream tasks when upstream dependencies fail).

4. **Plan-and-Execute agentic pattern** *(Workflow & Agentic Patterns)*
   - The system implements a full Plan-and-Execute pattern: the **orchestrator** (`mcp_server/src/app/orchestrator.py`) uses Gemini structured output to produce an `IncidentTriagePlan` with ranked hypotheses, phased tasks, and dependency ordering. The **phase runner** then executes the plan. The **synthesizer** (`mcp_server/src/app/synthesizer.py`) composes the final incident report. This three-stage pipeline (plan -> execute -> synthesize) is wired together in the composition root (`mcp_server/src/workflows/main_workflow.py`).

5. **Human-in-the-loop validation** *(Human-in-the-Loop & UX)*
   - The system generates patch proposals (e.g. rollback diffs, runbook updates) as structured artifacts and explicitly waits for human approval before applying them. The approval workflow uses `request_patch_approval` to create a pending proposal, `set_patch_approval` for the human to accept or reject, and `apply_approved_patch` to apply only after explicit approval. This goes beyond simple prompt feedback by implementing a full "AI Generation -> Diff Preview -> Human Review -> Gated Application" pattern.

6. **Multi-agent orchestration** *(Multi-Agent Systems)*
   - The system implements a manager-worker pattern where the **orchestrator** acts as a manager agent that decomposes the problem and assigns tasks, and **7 specialized worker agents** (`LogsQueryWorker`, `MetricsQueryWorker`, `DeploysListWorker`, `IncidentContextWorker`, `RunbooksSearchWorker`, `RunbookGetWorker`, `PatchGenerateWorker`) collaborate to investigate different aspects of the incident. The **worker registry** (`mcp_server/src/app/worker_registry.py`) maps tool names to worker instances, and the **synthesizer** aggregates outputs from all workers into a unified report.

7. **Observability with Opik** *(Observability & Evaluation)*
   - Both the MCP server (`mcp_server/src/utils/opik_utils.py`) and the MCP client (`mcp_client/src/utils/opik_handler.py`) integrate with Opik for LLM call tracing, tool invocation tracking, and agent behavior analysis. All MCP tools are decorated with `@opik.track()` for automatic span creation.

8. **Custom domain-specific data** *(Custom Data)*
   - Three incident scenario datasets are provided in `data/scenarios/`: `bad_deploy`, `dependency_latency`, and `saturation_db_pool`. Each scenario includes structured logs (`logs.jsonl`), time-series metrics (`metrics.json`), deployment records (`deploys.json`), and runbooks (`runbooks.md`). These are consumed by `mcp_server/src/mcp_tools/sre_guardian.py` to provide realistic, domain-specific incident data for investigation.

9. **Structured outputs with Pydantic models** *(Structured Outputs)*
   - The orchestrator uses Gemini's structured output mode with Pydantic response schemas to ensure consistent, validated responses. Key models include `IncidentTriagePlan`, `Task`, `Hypothesis`, `Severity`, `WorkerResult`, and `WorkerStatus` (all defined in `mcp_server/src/entities/`). This guarantees that LLM outputs conform to expected shapes and can be reliably consumed by downstream components.

---

## Contributing

Open an issue or PR with a clear problem statement, reproduction steps, and proposed changes.

## License

This project is licensed under the MIT License. See [LICENSE](LICENSE).

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