Amp
An agent platform whose agents can schedule wake-ups, retain context, and trigger workflows. Useful for PMs exploring persistent, scheduled AI automation tied into collaboration tools.
Key Highlights
- Amp is evolving from a coding assistant into a platform for persistent, scheduled, multi-agent automation.
- Its agents can retain context, wake themselves on a schedule, and trigger workflows across Slack, Puck, and other agents.
- Amp gives teams operational controls like remote runners, configurable compute environments, and custom agent plugins.
- The platform is especially relevant to PMs exploring async AI workflows for engineering, incident response, and internal operations.
- Recent updates show a strong emphasis on balancing model capability, speed, cost, and collaboration-native execution.
Amp
Overview
Amp is an agent platform focused on persistent, workflow-oriented AI automation. Its agents are designed to do more than answer prompts in a single session: they can retain context, wake themselves on a schedule, spawn additional agents, and operate across collaboration and execution environments like Slack, Puck, Terraform, and remote runners. Across recent product updates, Amp has increasingly looked like an operating layer for long-lived AI work rather than a simple chat interface.For AI Product Managers, Amp matters because it points to a practical pattern for production agent systems: asynchronous agents with memory, scheduling, tool access, environment-aware execution, and human-team integrations. The platform’s updates suggest a strong emphasis on real operational use cases such as bug triage, infra monitoring, code review, async collaboration, and automated follow-up work. That makes Amp a useful reference for PMs evaluating how AI agents can move from copilots to persistent teammates.
Key Developments
- 2026-05-26: Amp’s painter tool switched to GPT Image 2, which the company said improved screenshot and UI-edit fidelity while preserving text and style at lower cost.
- 2026-06-05: Opus 4.8 replaced Opus 4.7 in Amp’s smart mode, improving task completion in internal evals and producing tighter edits with better tool usage.
- 2026-06-06: Amp reported major speed improvements in deep and rush modes, including much faster first-token latency and meaningful end-to-end gains on long-horizon tasks.
- 2026-06-17: Amp introduced in-product diff review for thread code changes on desktop and mobile, including section-level feedback and interactive staging while environments are active.
- 2026-06-20: Amp launched custom agents via plugins, allowing teams to define specialized agents, use them as main or subagents, include them in tool pipelines, and spawn up to 25 worker agents.
- 2026-07-04: Amp expanded orb infrastructure options with four environment sizes, giving teams more control over compute and cost for agent execution.
- 2026-07-09: Amp added remote thread creation, enabling users to start new agents remotely from any Amp client and run headless runners in different working directories.
- 2026-07-10: Amp replaced named agent modes with a four-level capability dial—low, medium, high, ultra—mapped to different model and reasoning-effort combinations.
- 2026-07-21: Amp’s Slack integration enabled teams to mention `@Amp` to interact with personal Puck workflows for debugging, log retrieval, commit analysis, fixes, teammate notification, and thread archiving.
- 2026-07-22: Amp agents gained the ability to schedule their own wake-ups with saved prompt, full context, and history intact, plus integration with Slack, Puck, and spawning other agents for recurring operational workflows.
Relevance to AI PMs
1. A strong example of persistent agent design: Amp shows how agents can move beyond one-shot prompting into scheduled, stateful automation. PMs designing AI assistants for operations, support, engineering, or analytics can use Amp as a benchmark for features like memory continuity, wake-up scheduling, and background execution.2. A blueprint for collaboration-native AI workflows: The Slack and Puck integrations show how agents can fit into existing team processes instead of requiring users to adopt a separate AI interface. PMs can study this pattern when prioritizing where agents should surface—chat tools, incident threads, project workspaces, or code environments.
3. Useful lessons in controllability and platform packaging: Features like the capability dial, custom agents, orb sizing, diff review, and remote runners illustrate product decisions around cost/performance tuning, governance, and usability. PMs can apply these ideas when defining tiered agent capabilities, infra controls, and specialized agent templates for different teams.
Related
- Slack and Puck: Core collaboration surfaces for Amp’s team-facing workflows, especially debugging, notifications, and recurring reports.
- Terraform: Appears in Amp’s operational automation examples, suggesting infrastructure and production workflows are a key use case.
- OpenAI, Anthropic, Claude, Claude Opus 4.5/4.7/4.8, Claude Fable 5, GPT-5.4/5.5/5.6 Sol/5.6 Terra, and GLM-5.2: These model providers and model variants underpin Amp’s capability modes and orchestration strategy.
- Deep mode and rush mode: Earlier named operating modes that emphasized different speed/reasoning tradeoffs before the capability dial replaced them.
- Orb: Amp’s execution environment abstraction, with multiple compute sizes for agent runtime.
- Custom agents and diff algorithm: Important platform primitives that show Amp is building both orchestration and review infrastructure around agent-driven work.
- Chronicle / Ampcode Chronicle: The recurring source of product updates documenting the company’s feature releases and positioning.
- Humanlayer and PromptLayer: Related entities in the broader AI tooling and agent operations ecosystem.
- Greg Isenberg, Ryan Carson, and Simon Willison: Related people/entities connected in the source graph and potentially relevant for ecosystem context.
Newsletter Mentions (15)
“Amp agents can now set schedules to wake themselves with their saved prompt, full context, and history so they continue exactly where they left off, and these scheduled wake-ups integrate with Slack, Puck, and spawning other agents.”
Amp agents can now set schedules to wake themselves with their saved prompt, full context, and history so they continue exactly where they left off, and these scheduled wake-ups integrate with Slack, Puck, and spawning other agents. Example uses include a morning task that digs up the five slowest database queries from the past 24 hours and DMs the results on Slack, an hourly inference-error-triage that groups errors and spins up fix threads reported to #bugs, and ten-minute checks on long-running backfill jobs that ping if they stall or error.
“Amp's Slack integration lets teams mention @Amp to send messages to their personal Puck, which can read screenshots to reproduce bugs and post fixes, identify culprit commits and pull production logs to push fixes to Terraform, notify teammates and archive threads, and search Slack for relevant discussion.”
The Ampcode Chronicle describes Amp’s Slack-based workflow for debugging and team collaboration.
“Amp replaced named agent modes with a four-position capability dial — low, medium, high, ultra — switchable with Ctrl+S or the web picker, and wired to specific model + reasoning-effort stacks: ultra = Claude Fable 5 (writer) with GPT-5.6 Sol as oracle; high = GPT-5.6 Sol at xhigh with Claude Fable 5 as oracle; medium = GPT-5.6 Sol at medium (with a high-effort Sol oracle); low = GLM-5.2 (admins may opt for GPT-5.6 Terra low) with GPT-5.6 Sol as oracle.”
The Chronicle item explains how Amp maps different models and reasoning levels to a single capability dial.
“Amp now lets you start new agents remotely from anywhere you can run amp; enable remote thread creation with the command amp: enable remote creation of threads or by setting amp.remoteThreadCreation.enabled to true in ~/.config/amp/settings.json, after which every Amp client will accept and run new threads in its working directory.”
Ampcode Chronicle Agents, Anywhere - Amp now lets you start new agents remotely from anywhere you can run amp; enable remote thread creation with the command amp: enable remote creation of threads or by setting amp.remoteThreadCreation.enabled to true in ~/.config/amp/settings.json, after which every Amp client will accept and run new threads in its working directory. Use runner mode with amp --no-tui to run headless runners that only wait to start and run new threads; you can run multiple runners on the same machine if started in different directories, each runner identified by host and working directory, and directories need not be version controlled.
“Amp now offers four orb sizes: a0.tiny (1 CPU, 2GB memory, 40GB disk, $0.10/hour), a0.small (2 CPUs, 4GB, 40GB, $0.21/hour), a0.medium (8 CPUs, 16GB, 40GB, $0.83/hour) and a0.large (16 CPUs, 32GB, 40GB, $1.66/hour) which is the default; orb storage has been doubled from 20GB to 40GB at no extra cost and you can change a project's orb size in Project Settings.”
#8 📝 Ampcode Chronicle More Orb Sizes - Amp now offers four orb sizes: a0.tiny (1 CPU, 2GB memory, 40GB disk, $0.10/hour), a0.small (2 CPUs, 4GB, 40GB, $0.21/hour), a0.medium (8 CPUs, 16GB, 40GB, $0.83/hour) and a0.large (16 CPUs, 32GB, 40GB, $1.66/hour) which is the default; orb storage has been doubled from 20GB to 40GB at no extra cost and you can change a project's orb size in Project Settings.
“You can create custom agents in Amp via plugins and use them as main agents or subagents, include them in tool pipelines or spawn up to 25 worker agents, with each agent getting a custom orb color.”
#5 📝 Ampcode Chronicle Custom Agents - You can create custom agents in Amp via plugins and use them as main agents or subagents, include them in tool pipelines or spawn up to 25 worker agents, with each agent getting a custom orb color. The examples show amp.createAgent using model openai/gpt-5.5, registering a focused_review tool and an agent mode, and demonstrate thread APIs—createThread, appendUserMessage (returns immediately), waitForResponse—and an async start_async_review tool that spawns a background thread and returns Started background review in ${thread.id}.
“#16 📝 Ampcode Chronicle Diffs - Amp now lets you review any thread's code changes directly in Amp on desktop or mobile, scroll through diffs, request changes on specific sections, and interactively stage edits while a thread has an active environment.”
#16 📝 Ampcode Chronicle Diffs - Amp now lets you review any thread's code changes directly in Amp on desktop or mobile, scroll through diffs, request changes on specific sections, and interactively stage edits while a thread has an active environment. The diff algorithm detects duplicate blocks to highlight true changes (example: showing only the removed if-branch), and you can open the current thread's diff in your browser from the terminal via the command palette shortcut Ctrl‑O.
“Amp's deep and rush modes now deliver the first token 87% faster and p50 full responses 32% faster, mainly by switching to WebSockets for OpenAI communication and from a rebuild of Amp last month; on long-horizon tasks end-to-end speedups reach up to 40%.”
#20 📝 Ampcode Chronicle Faster Deep & Rush - Amp's deep and rush modes now deliver the first token 87% faster and p50 full responses 32% faster, mainly by switching to WebSockets for OpenAI communication and from a rebuild of Amp last month; on long-horizon tasks end-to-end speedups reach up to 40%.
“Opus 4.8 replaces Opus 4.7 in Amp's smart mode and solved 62% of tasks in Amp's internal evals (up from 52% for 4.7), running tests and code 15% more per task while making tighter, more focused edits.”
#14 📝 Ampcode Chronicle Opus 4.8 - Opus 4.8 replaces Opus 4.7 in Amp's smart mode and solved 62% of tasks in Amp's internal evals (up from 52% for 4.7), running tests and code 15% more per task while making tighter, more focused edits. It reaches for external tools more appropriately—calling librarian 14 times versus 1 for 4.7 and using edit_file for 79% of file edits (up from 63%)—drops the Read tool, and adds a ~2.5× fast mode that costs 2× base tokens (down from 6× on 4.7).
“#11 📝 Ampcode Chronicle GPT Image 2 Paints Better - Amp's painter tool now uses GPT Image 2, which Amp claims outperforms Gemini 3 Pro Image at preserving existing text, typography, and visual style when editing UI screenshots while costing roughly one-quarter the price.”
#11 📝 Ampcode Chronicle GPT Image 2 Paints Better - Amp's painter tool now uses GPT Image 2, which Amp claims outperforms Gemini 3 Pro Image at preserving existing text, typography, and visual style when editing UI screenshots while costing roughly one-quarter the price. In an example thread, Painter converted a screenshot of the Chronicle page into an updated design while retaining its original visual style.
Related
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A prompt management and AI workflow company. The newsletter cites its blog post arguing that fine-tuning is often the wrong default compared with RAG and other methods.
An entrepreneur and creator featured in a segment about making money with a Grok bot workflow. He is associated here with commentary on AI-driven newsletter operations.
A workplace messaging and collaboration platform. In this newsletter it appears as an integration target for AI setup and automation.
A model used as an automated judge in Claire Vo’s benchmark. It contributes 30% of the scoring alongside her manual evaluation.
A Claude model version referenced for its prompt-injection resistance metrics. It serves as a benchmark example of model-layer defenses being strong but not sufficient on their own.
A Claude model variant being updated with stronger biology safeguards to reduce false positives while still routing dual-use biology requests to higher-safety fallback behavior. Relevant for PMs considering safety tradeoffs and product-surface-specific policy tuning.
An OpenAI model or demo highlighted for high token throughput. The newsletter mentions it in the context of real-time agentic workflows and latency.
A GPT model variant used here for scientific reasoning and agentic chemistry experimentation. The newsletter frames it as a model capable of proposing experimental improvements and driving benchmarked workflows.
A Claude model variant referenced in Anthropic's cybersecurity evaluation report. It is one of the models involved in the incidents described.
An AI coding IDE and collaboration platform focused on tasks, agent sessions, artifacts, and workflows from questions through implementation. It is positioned as a productivity platform for engineers using AI APIs and agents.
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