AI Agent Development Roadmap
From first LLM call to production multi-agent systems
Blogging about tech is easy — building is hard. This is the skill progression we use to benchmark our AI agent, and the one we write about on this blog: structured outputs → tool calling → MCP → memory → agents → evals → observability → production. Each phase links to the guides that prove the pattern in production.
Foundation — LLM APIs & Context Engineering
Get reliable, schema-validated output from a single LLM call before you add tools.
Write prompts that stay robust to model drift — not demos that only work once.
JSON schemas and typed responses so the model returns data your code can trust.
Know what to put in, what to leave out, and when to compress.
System prompts and few-shot examples as a deliberate context budget.
Outcome: Build a single LLM call that returns reliable, schema-validated data for a real task.
Tool Calling
Give your agent the ability to act on the world through functions and APIs.
Design a tool registry with clear schemas and error contracts.
Build tools an LLM can invoke correctly — good agent-computer interfaces.
Turn terminal commands into callable, testable agent tools.
Know when plain functions, MCP servers, or agent protocols are the right call.
Outcome: Give an agent 3-5 tools and have it reliably complete a multi-step task.
MCP — Model Context Protocol
Build, test, and observe your own MCP servers as reusable agent capabilities.
Implement a server with proper tool, resource, and prompt definitions.
Automate protocol-level tests before a server ever reaches an agent.
Trace tool calls and log inputs/outputs so failures are debuggable.
Deploy, secure, and scale servers agents depend on.
Understand MCP-specific attack surfaces like tool poisoning.
Outcome: Ship a production-grade MCP server that other agents can discover and use.
Memory & Context Optimization
Manage context and memory so agents don’t lose the plot — or the budget.
Short-term and long-term stores that persist useful state across turns.
Sliding windows, summarization, and priority eviction for long sessions.
Engineer prompts to maximize cache hits and cut latency.
Multi-tier caching and token budgets as a design constraint.
Outcome: An agent that remembers prior turns and answers faster and cheaper.
Single-Agent Loop
Turn the agent loop into a reliable, error-resistant system.
OpenAI Agents SDK, Claude Agent SDK — know what the loop handles for you.
Retries, fallbacks, and circuit breakers for API and tool failures.
Detect and stop agents stuck in repetitive failure loops.
Recover long-running agents after crashes and context compaction.
Respect quotas without killing throughput.
Outcome: Run a single agent in production that fails gracefully and recovers.
Multi-Agent Orchestration
Coordinate multiple agents to solve problems no single agent can.
Orchestrator-worker, routing, parallelization — pick per task shape.
LangGraph, CrewAI, OpenAI SDK: what each abstraction buys and hides.
Standardized agent-to-agent communication between teams.
Know the 7 ways multi-agent systems break in production.
Message queues and durable coordination between workers.
Outcome: Design and run a multi-agent system with clear roles and reliable handoffs.
Evaluation
Prove your agent works before you ship it — and catch regressions after.
A test suite that runs your agent against realistic scenarios.
Score outputs with a separate model — and know its biases.
DeepEval, LangSmith, or custom: what fits your stack.
Measure against SWE-bench and task-specific baselines.
Outcome: Measure agent quality and catch regressions before users do.
Observability
See what your agent is doing in production — every step, tool call, and token.
Trace every LLM call, tool call, and agent step end to end.
OTel-compatible instrumentation with OpenInference spans.
Track quality over time, not just latency and errors.
Watch token spend per run and per phase.
Outcome: Debug a failed agent run in minutes, not hours.
Production Hardening
Make your agent secure, safe, governed, and ready for real users.
Prompt injection, dataset poisoning, and supply-chain attack surfaces.
Automated input/output safety checks that run before and after the model.
Approval workflows for high-stakes actions.
Containerize and deploy agents with reproducible builds.
LLM routers and fallbacks that keep spend under control.
Outcome: Deploy an agent that is secure, safe, observable, and cost-controlled.
Completed Builds — Proof the Agent Ships
Eleven classic web apps our agent has already built end-to-end. They are the benchmark under this roadmap — each one taught us a pipeline lesson.
The benchmark continues: AI-era builds — MCP server scaffolder, tool-call chain analyzer, multi-agent dashboard, LLM router configurator — are queued next. This roadmap tracks the skills behind them.