Built at The Agent Harness Hackathon

AgentFlight

# AgentFlight ## Production Control Tower for Governed AI Agents AgentFlight is a production-minded AI operations agent built with TrueForge, OpenAI, MCP, and Daytona. Instead of treating a successful tool call as successful task completion, AgentFlight follows an evidence-first operational loop: **Observe → Diagnose → Prepare → Govern → Execute → Verify → Recover/Escalate** ## Problem AI agents can reason and call tools, but production deployment introduces harder problems: - How do we coordinate models, tools, skills, and permissions? - How do we control production-changing actions? - What happens when a tool or deployment fails? - How do we know an action actually solved the problem? - How can operators inspect what the agent did? A production agent should not report success simply because an API call returned successfully. ## Solution AgentFlight uses TrueForge as the agent harness around an OpenAI-powered operations agent. The agent: 1. Inspects an incident using operational evidence. 2. Separates observed facts from inferred root cause. 3. Prepares a remediation before changing production. 4. Exposes the proposed action and risk. 5. Executes production changes through MCP tools. 6. Evaluates post-deployment health evidence. 7. Declares success only when verification confirms recovery. 8. Stops or escalates when execution or verification fails. ## Architecture User / Incident Request ↓ TrueForge Agent Harness ↓ OpenAI Reasoning ↓ AgentFlight Operations Skill ↓ MCP Operations Server ├── inspect_incident() ├── prepare_fix() └── deploy_fix() ↓ Post-action Verification ↓ Resolved / Stop / Escalate ## TrueForge Integration TrueForge provides the orchestration layer for AgentFlight: - OpenAI model execution - Agent instructions and operational skill - MCP tool integration - Daytona sandbox support - Agent sessions and execution visibility - Tool-call and outcome inspection - Runtime configuration The custom `agentflight-operations` skill defines the evidence, remediation, execution, verification, and failure-handling workflow. ## MCP Operations Tools ### inspect_incident Read-only investigation of production evidence including error rate, latency, database utilization, logs, and deployment context. ### prepare_fix Creates the proposed remediation and risk assessment without changing production. ### deploy_fix Represents the production-changing operation and returns deployment and post-deployment verification evidence. ## Evaluation Scenarios ### INC-204 — Successful Recovery Initial evidence: - Error rate: 18.7% - p95 latency: 4.8 seconds - Database pool utilization: 100% - Connection pool size: 20 AgentFlight identifies database connection pool exhaustion and prepares an increase from 20 to 40 connections. Post-deployment verification: - Error rate: 18.7% → 0.8% - p95 latency: 4.8 s → 620 ms - Database pool utilization: 100% → 51% Result: **RESOLVED** ### INC-500 — Deployment Failure The remediation is prepared, but the deployment provider times out. AgentFlight: - Does not claim deployment success - Reports `DEPLOY_PROVIDER_TIMEOUT` - Confirms production was not changed - Does not fabricate verification evidence - Stops safely and escalates Result: **STOP / ESCALATE** ### INC-501 — Successful Deployment, Failed Outcome The deployment API succeeds, but post-deployment health becomes worse: - Error rate: 18.7% → 21.4% - p95 latency: 4.8 s → 5.2 s - Database pool utilization: 100% → 100% AgentFlight recognizes that deployment success is not remediation success. Result: **UNHEALTHY — ESCALATE / CONSIDER ROLLBACK** ## Key Differentiator **Execution success does not equal outcome success.** AgentFlight verifies the operational outcome of an agent's actions before declaring the objective complete. This pattern can extend beyond incident response to infrastructure, software engineering, finance, support, security, and other enterprise agents where actions must be observable, controlled, and verified. ## Built With - TrueForge / TrueFoundry - OpenAI - Model Context Protocol (MCP) - FastMCP - Python - Daytona Sandbox - GitHub ## Repository https://github.com/helshahaby/agentflight ## Author Hossam Elshahaby