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Sentry: Autonomous Self-Healing CI/CD Repair Agent
THE PROBLEM: THE ASSERTION PROXIMITY TRAP IN CI/CD Every day, engineering teams lose thousands of hours to broken CI/CD pipelines. Existing AI coding assistants fail at autonomous CI repair because of the Assertion Proximity Trap: where a test assertion fails is almost never where the bug actually lives. A test fails in tests/test_pricing.py, which calls order_service/pricing.py, which imports order_service/discount.py. Naive AI tools inspect only the failing test file, hallucinate unverified patches, or delete test assertions to force a fake green build. THE SOLUTION: SENTRY Sentry is an autonomous, self-healing CI/CD repair agent built on the IBM Bob 2.0 Gateway and IBM Granite (granite-8b-code-instruct). It operates as a deterministic 8-state finite state machine with closed-loop sandbox verification: 1. INGEST AND REPRODUCE: Clones the repository into an isolated sandbox (Docker or Local) and executes the test suite (pytest, unittest, node --test, Jest) to capture baseline counts and stack traces. 2. NAVIGATE (4-Hop AST and Import Traversal): Extracts stack frames and traverses Python and Node.js module imports up to 4 hops deep, feeding the call chain to IBM Granite 8B Code Instruct while strictly forbidding edits to test files. 3. HYPOTHESIZE AND PATCH: Ranks suspect files by confidence score (0-100%), formulates a root-cause hypothesis, and generates a minimal unified diff. 4. VALIDATE (Closed-Loop Retry up to 3 Attempts): Applies the patch and re-runs the full test suite in the sandbox. If validation fails, Sentry reverts the workspace via git checkout and feeds the error trace back into the model. 5. PR_CREATE VS. REPORT_BEST_EFFORT (Anti-Hallucination Guarantee): - RED TO GREEN: Automatically creates a fix branch, pushes to GitHub, and opens a Pull Request with a before-and-after verification matrix. - Unresolved After 3 Attempts: Never fabricates success. Compiles an honest Best-Effort Diagnostic Report (suspects, attempted diffs, and hypotheses) for human engineers. ARCHITECTURE AND TECH STACK - AI Layer: IBM Granite (granite-8b-code-instruct) and IBM watsonx Orchestrate routed through IBM Bob 2.0 Gateway with multi-model failover. - Backend: Deterministic 8-state Python FSM bridged to a FastAPI async WebSocket server (/ws). - Sandbox: Pluggable DockerSandbox and LocalSandbox with multi-runner output parsers. - Frontend: Next.js 14 (App Router, TypeScript, Tailwind CSS) real-time dashboard with live state timeline, streaming event log, confidence bars, and unified diff viewer. VERIFIED BENCHMARKS 1. Python (hero_python): Traces 2 hops from test_pricing.py into discount.py (90% confidence), fixes 0.8 multiplier to 0.2, validates 3/4 RED to 4/4 GREEN on Attempt 1, and opens GitHub PR #1. 2. Node.js (hero_node): Traces CommonJS modules into token_validator.js (92% confidence), fixes clock-skew operator (- to +), and validates 2/3 RED to 3/3 GREEN on Attempt 1. 3. Honest Failure: Reverts all 3 failed attempts on unfixable bugs and exports a diagnostic trail with 0% false positive claims.
27 Sep 2026
Evie - Electronic Volatility Intelligence Entity
The Strategy EVIE (Electronic Volatility Intelligence Entity) is an autonomous quant engine. Instead of analyzing options charts directly, EVIE uses an Institutional Proxy Execution Model. She evaluates liquid underlying equities on 5-minute candles, searching for a 9/21 EMA crossover and RSI momentum (55-70). When confirmed, she queries the Alpaca API and dynamically routes the execution to the nearest At-The-Money (ATM) weekly call contract. Gemini AI Risk Gate Every signal must pass a Gemini LLM Risk Gate. Unlike basic bots, EVIE’s default stance is NO. She actively hunts for mathematical reasons to reject fakeouts. Crucially, EVIE possesses contextual memory. If she recently sold a position, the exact exit price and timestamp are injected into Gemini’s prompt. This allows the AI to logically distinguish between a valid new setup and a dangerous "re-entry chase." Risk Management Sizing: 12.5% equal-weight allocation per ticker. Exits: A hard +15% Take Profit and -15% Trailing Stop Loss from the high-water mark. Execution: Market orders guarantee immediate fills during momentum spikes, prioritizing execution certainty over theoretical pennies. FastMCP & Infrastructure EVIE streams real-time trade logs and P&L to a private Discord webhook. Furthermore, EVIE operates as a FastMCP server. By exposing tools like get_market_signals and force_square_off, human operators can use Claude Desktop or Cursor to ask EVIE for live quant readouts in natural language, or trigger emergency liquidations if the macro market turns hostile.
4 Sep 2026
