How I built an AI-native platform for building, deploying, and managing intelligent agents — with circuit breakers that prevent runaway costs and time-travel debugging that replays any run.
Teams building AI agents lack guardrails for cost, debugging, and behavioral consistency. Most agent frameworks stop at "it works on my machine" — leaving production deployment as an unsolved problem.
AI agents can loop indefinitely, calling expensive models with no upper bound. Budget caps alone aren't enough — you need circuit breakers that detect and stop pathological behavior.
When an agent makes a wrong decision, understanding why is nearly impossible without replay capabilities. You're left guessing at what the model saw and thought.
Most agent frameworks are designed for prototyping. They lack the execution strategies, error handling, and deployment infrastructure needed for production.
A full-stack platform with a React SPA frontend, FastAPI backend, PostgreSQL persistence, and Docker-based deployment.
A single-page application using React 19 with tagged template literals and GSAP-powered scroll animations. Communicates with the backend through 557 REST API endpoints.
A modular FastAPI backend with PostgreSQL persistence, Redis caching, and Docker orchestration. 7 execution strategies handle different agent workloads.
Agent Request ├── Router selects execution strategy (7 available) │ ├── Direct — single model call, no loop │ ├── Reflexion — iterative self-critique │ ├── Tool Use — function calling with external APIs │ ├── Orchestrator — sub-agent delegation │ ├── Workflow — DAG of sequential steps │ ├── Debate — multi-model consensus │ └── Parallel — fan-out independent agents ├── Circuit Breaker monitors cost & behavior │ ├── Token budget exceeded → soft stop │ ├── Loop detected → hard stop with trace │ └── Anomaly score > threshold → escalate ├── Execution logged to time-travel store └── Response returned + trace available for replay Storage ├── PostgreSQL — users, agents, runs, configs ├── Redis — job queue, rate limits, session cache └── Time-Travel Store — full execution traces
Real metrics from a production AI agent platform — not a demo, not a prototype.
RESTful endpoints across agents, workflows, users, configs, and monitoring
From single model calls to multi-agent debates and parallel execution
Circuit breakers, token budgets, and behavioral anomaly detection
Every execution trace stored for time-travel debugging
React 19 SPA with GSAP animations, bilingual, PWA-enabled
Async Python with auto-generated OpenAPI spec
Compose-based with PostgreSQL, Redis, and app containers
Full bilingual support across the entire platform
A complete agent orchestration platform — not a framework, not a library, a product.
Route requests to the optimal model based on task type, cost budget, and latency requirements. BYOK support for custom endpoints.
Compose multiple agents into workflows. Each agent has its own model, instructions, and tool set. Orchestrator delegates and aggregates results.
Three safeguard layers: token budget caps, loop detection, and behavioral anomaly scoring. Each can soft-stop, hard-stop, or escalate.
Every execution trace is stored. Replay any run step-by-step to see exactly what the model saw, thought, and returned. No more guessing.
Docker Compose-based deployment that runs anywhere — VPS, dedicated server, or local machine. Full data sovereignty, no vendor lock-in.
Define behavioral contracts for agent outputs. Auto-assertions catch regressions before they reach users. Integrates with the replay system.
Budget caps set a hard limit on cost, but they don't prevent the behavior that causes high cost. Circuit breakers detect pathological patterns — loops, spiraling tool calls, rapidly degrading quality — and stop the agent before it burns through the budget.
Stops spending after N tokens. Agent fails mid-task with no recovery. User has to re-run and hope it's cheaper this time.
Detects the loop, terminates it, stores the trace for debugging. Engineer replays, identifies the root cause, fixes the agent. Done.
The real unlock is coupling circuit breakers with replay. You don't just stop the failure — you understand it. Then you fix it and move on.
I build production-grade agent orchestration systems. From strategy through architecture to deployment — with circuit breakers, replay debugging, and sovereign infrastructure.