Pin models and tune reasoning effort per task
Antigravity 1.1.5 adds stable model slugs, /effort and --effort controls, a redesigned /model picker with effort gauges, and per-agent model frontmatter. Use them to pin reproducible model choices and trade latency for depth on the fly instead of accepting defaults.
Why this loop
This release makes model identity and reasoning depth first-class and session-stable: slugs work in /model and --model, /effort and --effort switch effort variants with a timeline gauge and status-line badge, and custom agents can declare model (flash, pro, or inherit) in frontmatter. Teams that standardize slugs and effort per task type get repeatable CLI launches and subagent behavior, fewer “wrong model” surprises, and intentional latency-vs-depth tradeoffs. Reliability fixes (MCP embedded resources, permission parsing with quoted metacharacters, background-task lifecycle) further support leaning on MCP tools and longer sessions once model/effort policy is explicit.
Proposed actions
- Adopt stable model slugs in scripts and docs via --model and the /model picker so every session pins the same base model.
- Use /effort or --effort to raise reasoning depth for design/debug work and lower it for routine edits; confirm the status-line effort badge before long runs.
- Set model in custom agent frontmatter (flash, pro, or inherit) so subagents run at the intended tier instead of silently inheriting.
- Re-test MCP servers after upgrade: authenticate from /mcp, and verify tools that return embedded resources now surface text and inline media.
- Revisit permission allowlists for commands with quoted shell metacharacters (e.g. --grep="a|b") so pipelines no longer trigger spurious prompts.
Action pack
Paste into your agent or query via MCP (`get_action_pack`) — free, no extra AI costGoogle Antigravity / agent task
# DevAgentRadar → Google Antigravity Goal: turn this release signal into a safe pilot plan for my stack. ## Context Assistant: Google Antigravity Proposal: Pin models and tune reasoning effort per task Summary: Antigravity 1.1.5 adds stable model slugs, /effort and --effort controls, a redesigned /model picker with effort gauges, and per-agent model frontmatter. Use them to pin reproducible model choices and trade latency for depth on the fly instead of accepting defaults. Primary source: https://github.com/google-antigravity/antigravity-cli/releases/tag/1.1.5 ## Why it matters This release makes model identity and reasoning depth first-class and session-stable: slugs work in /model and --model, /effort and --effort switch effort variants with a timeline gauge and status-line badge, and custom agents can declare model (flash, pro, or inherit) in frontmatter. Teams that standardize slugs and effort per task type get repeatable CLI launches and subagent behavior, fewer “wrong model” surprises, and intentional latency-vs-depth tradeoffs. Reliability fixes (MCP embedded resources, permission parsing with quoted metacharacters, background-task lifecycle) further support leaning on MCP tools and longer sessions once model/effort policy is explicit. ## Suggested actions 1. Adopt stable model slugs in scripts and docs via --model and the /model picker so every session pins the same base model. 2. Use /effort or --effort to raise reasoning depth for design/debug work and lower it for routine edits; confirm the status-line effort badge before long runs. 3. Set model in custom agent frontmatter (flash, pro, or inherit) so subagents run at the intended tier instead of silently inheriting. 4. Re-test MCP servers after upgrade: authenticate from /mcp, and verify tools that return embedded resources now surface text and inline media. 5. Revisit permission allowlists for commands with quoted shell metacharacters (e.g. --grep="a|b") so pipelines no longer trigger spurious prompts. ## Config surfaces this release may change - subagent definitions — check your repo before applying - MCP servers — check your repo before applying - permission rules — check your repo before applying Please: 1. Map the change to concrete pilot steps 2. Flag security / permission implications 3. Keep the pilot reversible
Your loop
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