Aswin Kumar

As an Agentic AI Engineer, you must build these projects. 1.) Async Agent Orchestrator Build: A FastAPI + asyncio event loop handling 100+ concurrent agent tasks with non-blocking tool calls and graceful error recovery. Why: `while True` loops block the main thread. Real-world agents require event-driven concurrency to survive production traffic. 2.) Dynamic Context Budget Assembler Build: A pre-flight middleware that strictly allocates tokens across system prompts, retrieved docs and tool schemas per request. Why: Context overflow is the #1 silent killer of production agents. You must budget tokens like you budget cloud spend. 3.) Strict Pydantic Tool Router Build: A tool execution engine that forces LLM outputs into strict JSON schemas and automatically retries with the error trace on validation failure. Why: Agents hallucinate API parameters. Grammar-constrained decoding and Pydantic validation turn stochastic outputs into deterministic actions. 4.) 3-Tier Memory & Eviction Engine Build: A memory system with working scratchpads, episodic session compression and semantic vector search with automated TTL eviction. Why: Infinite context causes OOM errors and hallucinations. Memory must be actively managed, scored and pruned. 5.) Self-Healing ReAct Loop Build: An agent that reads its own stack traces, parses the compiler/API error, rewrites the failing tool call and retries up to 3 times. Why: Agents that fail on the first try are useless. Agents that debug themselves are digital employees. 6.) Typed Multi-Agent Swarm Build: A LangGraph state machine where a Researcher, Coder and Reviewer agent pass strictly typed state objects, never raw text. Why: Single agents fail on complex tasks. Swarms divide and conquer but raw text handoffs cause context drift. 7.) Async HITL Slack Gate Build: A workflow that drafts a high-stakes action, serializes its state and waits 3 days for a human to click "Approve" via a Slack webhook. Why: High-stakes automation requires human accountability. Agents must know how to pause, wait, and resume cleanly. 8.) Trajectory Eval CI Gate Build: A GitHub Action that replays 100 agent trajectories and blocks the PR if the tool-call sequence deviates from the golden DAG. Why: Grading the final text output hides the fact that the agent hallucinated an API call or skipped a safety check to get there. 9.) OpenTelemetry Agent Tracer Build: Custom tracing spans capturing every LLM hop, token count, tool latency and state mutation in LangSmith or Arize. Why: You cannot debug a 14-step agent workflow if you can't replay the exact sequence of events that led to the failure. 10.) Inter-Agent Sanitization Proxy Build: Middleware that intercepts messages between agents, stripping malicious instructions to prevent cross-agent prompt injection. Why: Multi-agent systems create internal attack surfaces. Agent A can be tricked into hijacking Agent B's tools. 11.) Sandboxed Code Executor Build: A code-interpreter agent that writes and executes Python in an isolated E2B or gVisor microVM with zero internet egress. Why: Letting an LLM run `os.system()` on your host machine is a catastrophic security risk. 12.) Cost Kill-Switch Middleware Build: A router tracking loop depth and projected spend that instantly halts the agent if it exceeds $0.50, falling back to a cheaper model. Why: A runaway agent loop is an outage with an invoice. It will burn $500 in API credits before you notice. 13.) API-to-Browser Fallback Agent Build: A routing layer where if the primary REST API 404s or times out, the agent seamlessly spawns a headless browser to finish the GUI workflow. Why: The real world is messy. 80% of enterprise software has no API. Agents must adapt to broken environments. 14.) Shadow Trajectory Comparator Build: A silent 5% traffic mirror that runs a new agent topology in the background, diffing tool-call paths and costs without affecting users. Why: You cannot A/B test non-determinism safely in production without shadow mode. 15.) Public Architecture Teardown Build: 3 published deep-dives detailing your swarm topology, state-machine design, inter-agent security and cost-per-task analysis. Why: Top AI labs hire Agent Engineers based on their system design judgment not their ability to import LangChain. Systems that prove you can orchestrate, secure & scale when autonomous agents touch your stack. Most people watch tutorials. Builders ship systems. Bookmark & Repost.