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 focuses on persistent agents that can retain context, schedule wake-ups, and resume work with full history.
- Its recent updates emphasize operational automation through Slack, Puck, remote runners, and multi-agent workflows.
- Amp abstracts model complexity behind a capability dial tied to different model and reasoning stacks.
- The platform also includes PM-relevant infrastructure choices such as configurable compute orbs and asynchronous custom agents.
- Amp's product direction is useful for AI PMs evaluating always-on agents rather than one-shot copilots.
At a glance
- Category
- Agent
Amp
Overview
Amp is a company building an agent platform centered on persistent, workflow-oriented AI agents. Its agents can retain prompt, context, and history across sessions, schedule their own wake-ups, trigger other agents, and integrate with collaboration surfaces like Slack and Puck. Across recent product updates, Amp has also expanded into remote agent execution, custom agent creation, diff-based code review, image editing for UI workflows, and configurable compute environments called orbs.For AI Product Managers, Amp matters because it represents a practical move from one-shot copilots toward always-on operational agents. The product surfaces patterns PMs increasingly need to evaluate: scheduled automation, asynchronous multi-agent work, human-in-the-loop debugging in collaboration tools, and tradeoffs between model capability, cost, latency, and infrastructure sizing. Amp is especially relevant for PMs exploring how agent systems fit into software delivery, incident response, internal tooling, and team workflows.
Key Developments
- 2026-05-26: Amp updated its painter tool to use GPT Image 2, claiming better preservation of text, typography, and visual style in UI screenshot edits at roughly one-quarter the price of Gemini 3 Pro Image.
- 2026-06-05: Opus 4.8 replaced Opus 4.7 in Amp's smart mode, improving internal task-solving performance, increasing test/code execution per task, and enabling more focused edits and more appropriate tool usage.
- 2026-06-06: Amp reported major speed improvements in deep and rush modes, including 87% faster time to first token and 32% faster p50 full responses, driven in part by WebSockets for OpenAI communication.
- 2026-06-17: Amp added in-product diff review for any thread on desktop or mobile, including section-specific change requests, interactive staging, and a diff algorithm designed to suppress duplicate blocks and show true code changes.
- 2026-06-20: Amp introduced custom agents via plugins, allowing teams to define main agents or subagents, include them in tool pipelines, and spawn up to 25 worker agents. The thread APIs also support asynchronous background work.
- 2026-07-04: Amp expanded orb infrastructure options with four sizes ranging from a0.tiny to a0.large, doubled storage from 20GB to 40GB, and made orb sizing configurable per project.
- 2026-07-09: Amp launched remote thread creation, letting users start new agents from anywhere they can run Amp. Headless runner mode supports distributed execution across machines and working directories.
- 2026-07-10: Amp replaced named agent modes with a four-position capability dial—low, medium, high, and ultra—mapped to different model and reasoning-effort combinations involving Claude Fable 5, GPT-5.6 Sol, and GLM-5.2.
- 2026-07-21: Amp's Slack integration enabled users to mention @Amp to interact with their personal Puck, including reading screenshots, reproducing bugs, identifying culprit commits, pulling logs, pushing fixes to Terraform, notifying teammates, and archiving threads.
- 2026-07-22: Amp agents gained scheduled wake-ups that restore saved prompt, full context, and history so they can resume work exactly where they left off. These schedules integrate with Slack, Puck, and spawning other agents for recurring operational tasks.
Relevance to AI PMs
1. Designing persistent agent workflows: Amp provides concrete examples of agents that do not end after a single interaction. PMs can study its scheduled wake-ups, background threads, and remote execution model when defining requirements for recurring automation such as monitoring, triage, or reporting.2. Evaluating model orchestration and capability tiers: Amp's capability dial shows one way to abstract multiple models and reasoning settings behind simpler UX. AI PMs can use this as a reference for packaging cost, latency, and quality tradeoffs without forcing users to understand model routing details.
3. Embedding agents into team operations: Amp's Slack, Puck, diff review, and Terraform-related workflows illustrate how agents become useful when attached to existing collaboration and delivery systems. PMs can apply this lesson when prioritizing integrations over standalone chat experiences.
Related
- Slack: A major integration surface for Amp, including @Amp mentions, thread workflows, notifications, and scheduled agent outputs.
- Puck: Used alongside Amp in collaboration and debugging flows, especially in the Slack-based workflow described in Chronicle updates.
- OpenAI / GPT-5.5 / GPT-5.6 Sol / GPT-5.6 Terra / GPT Image 2: Amp uses OpenAI models across coding, reasoning, and image-editing workflows, including its painter tool and capability dial.
- Anthropic / Claude / Claude Opus 4.7 / 4.8 / Claude Fable 5: Amp has incorporated Anthropic models into smart and ultra-style modes, highlighting a multi-model orchestration strategy.
- GLM-5.2: Used in Amp's lower-cost capability tier, showing how the platform mixes models for different budget/performance profiles.
- Orb: Amp's configurable compute environment for project execution, with multiple sizes and pricing tiers.
- Terraform: Appears in Amp's operational workflow examples, where agents can help push fixes as part of incident or debugging loops.
- Chronicle / Ampcode Chronicle: The newsletter-style product update stream where many of Amp's feature changes were described.
- Custom agents, deep mode, rush mode, diff algorithm, remote thread creation: These related product concepts capture Amp's emphasis on asynchronous execution, reviewability, speed, and extensibility.
Frequently asked questions
What is 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.
What should AI product managers know about Amp?
Amp focuses on persistent agents that can retain context, schedule wake-ups, and resume work with full history. Its recent updates emphasize operational automation through Slack, Puck, remote runners, and multi-agent workflows. Amp abstracts model complexity behind a capability dial tied to different model and reasoning stacks.
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
An AI company mentioned for releasing a report on model behaviors and for internal tool use in later context. Relevant for PMs tracking safety reporting and frontier model operations.
OpenAI is an AI company building foundation models and consumer products like ChatGPT. In this newsletter it is discussed in connection with GPT-6, Dots, ChatGPT for Teens, and College Planner.
Anthropic’s AI assistant, referenced here through new dashboard, motion, docs, slides, and design capabilities.
An OpenAI coding model/tool used here as part of coding harnesses turned into reinforcement-learning environments.
A prominent AI blogger and commentator referenced in connection with an article on token reselling and fraud. He is cited as the source of the newsletter item discussing the marketplace and API-key abuse.
Entrepreneur and AI workflow commentator mentioned presenting a masterclass on deploying AI agents. The newsletter frames him around workflow redesign and automation economics.
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.
Slack is a workplace messaging platform often connected to AI agents and internal tools. In this context it is one of the data sources powering an incident-command overview.
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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