descender

The month in one paragraph

February 2026 was the routing month. Agent workflows became normal enough that users no longer only asked "which model is best?" They asked which agent should handle which task, in what environment, with what tools, and under what review policy. GitHub Agent HQ made this product shape obvious by putting Copilot, Claude, Codex, and custom agents into the same developer workflow surface.

Actual field update

Multi-agent selection surfaced: users could choose among agents for issues, PRs, and tasks.
Background delegation normalized: agents increasingly ran as queued workers instead of synchronous assistants.
Plugin and connector surfaces expanded: capability moved through controlled tool access, not only larger models.
Sandbox access controls became central: where the agent can run became as important as what model powers it.

Robustness check

Strong claim: agent selection is becoming workload routing.

Strong claim: background execution requires sandboxing, state, and review.

Weak claim to avoid: "multi-agent always improves quality." Specialization helps only when task boundaries are real and outputs can be verified.

Agentic design pattern change

The emerging pattern:

task intake
→ classify task
→ select agent / model / tool policy
→ run in scoped environment
→ compare or review output
→ accept / reject / reassign

The product layer becomes an agent router.

Fallout

Agent marketplaces create governance pressure.
Routing policy becomes part of platform architecture.
Different agents produce different artifact shapes, which complicates review.
Cost control requires per-agent accounting.

What builders should copy

Route by task type, risk level, context need, and expected artifact.
Keep agent outputs normalized into a common review object.
Use cheap/specialized models for classification and routing where possible.
Avoid "agent roleplay" unless each role has tools, authority, and a verifiable output.

Resource sources

GitHub Agent HQ coverage: https://www.theverge.com/news/873665/github-claude-codex-ai-agents
Anthropic — Building Effective Agents: https://www.anthropic.com/engineering/building-effective-agents
OpenAI Agents SDK: https://developers.openai.com/api/docs/guides/agents
AIDev dataset: https://arxiv.org/abs/2602.09185

Operator math (TeX)

T_{\text{task}} = T_{\text{queue}} + T_{\text{agent-run}} + T_{\text{human-review}}

Field Notes: AI — February 2026