API-first was for developers. MCP-first is for agents.
API-first was a real step forward, but it answers a different question. Here is what MCP-first adds on top, and why it matters for agent-ready software.
Agents, MCP, and the small-model cost crash: where 2026 is heading
Three durable shifts reshaping how software gets built, agents moving into production, tool/context protocols standardizing, and small models getting good and cheap.
Design patterns for long-horizon agents
Patterns that keep multi-step, long-running agents reliable, task decomposition, sub-agent delegation, checkpoints and recovery, self-verification, budgets, and human gates.
Prompting for long-horizon reasoning: effort and self-checks
Techniques that make models reliable on long, multi-step tasks, decomposition, explicit effort budgets, scratchpads, and self-verification passes.
Loop engineering: designing the system that drives the agent
The model is only half the system. The other half is the loop around it, observe, plan, act, verify, retry, stop, and the guardrails that keep it honest.
Using LLM agents for large-scale code migrations
How to run a framework upgrade or codebase-wide refactor with agents, discovery, per-file transforms, verification, isolation, and review gates that keep it safe.
Browser-using agents: power and peril
Agents that drive a real browser can do almost anything a user can, which is exactly why they need capabilities, confirmation, and audit, not a free hand.
Multi-agent systems: when one agent isn't enough
Orchestrator/worker patterns, specialization, and shared context, plus the failure modes (cost blowups, loops, compounding errors) and how to contain them.
Function calling and tool use: how agents actually act
How models go from text to action through typed tool definitions, and why the quality of your tool schemas decides how reliable your agent is.