Category guide
AI agent launch context
AI agent launches cover tools that can plan, use tools, browse, code, operate workflows, or complete background tasks with some autonomy.
What belongs here
Browser agents, workflow agents, enterprise agent platforms, coding agents, background task agents, and agent infrastructure with verifiable product or release links.
Why this matters
Agent claims can be noisy, so source links, demos, permissions, API access, and clear human-review boundaries matter more than broad autonomy language.
For AI companies
Turn a launch into source-backed visibility
Kingy AI uses launch records, tool profiles, Daily Launch Radar coverage, creator-fit signals, and ROI tools to help AI companies move from announcement to useful discovery.
OpenAI Assistants API was sunset on August 26, 2026
OpenAI’s Assistants API reaches its shutdown deadline on August 26, 2026. OpenAI recommends Responses API and Conversations API, with documented mappings from Assistants to configuration or prompt structures,…
Treat August 26 as an operational deadline, not a feature launch or a find-and-replace exercise. OpenAI documents object and orchestration changes, including explicit tool-loop…
Anthropic retired Claude Opus 4.1 on August 5, 2026
Anthropic announced the retirement of claude-opus-4-1-20250805 on the Claude API for August 5, 2026 and recommends claude-opus-4-8. Anthropic notes that partner-platform schedules can differ from its operated API…
Treat August 5 as a model retirement deadline, not a product launch or a blind model-ID replacement. Inventory traffic by provider, confirm which schedule…
Liquid AI LFM2.5-2.6B
Liquid AI released the base and post-trained LFM2.5-2.6B checkpoints on August 4, 2026. The post-trained text model targets agentic workloads and ships in native, GGUF, MLX, and ONNX…
- Launch readiness
- 9.2 / 10
- Demo evidence
- 7.5 / 10
- Creator-story fit
- Medium
Score definitions and rubric
These are launch-record readiness heuristics, not product ratings.
Launch readiness
How complete and reviewable the launch record is, not the quality of the product.
Inputs and weights: Launch date 15%; qualifying source 10%; what launched 10%; demo 15%; category 10%; audience 10%; editorial assessment 10%; traction evidence 10%; creator or audience fit 10%.
Evidence inputs: Reviewed launch metadata, public source links, demo links, taxonomy, audience, editorial notes, and recorded traction signals.
Demo evidence
Whether the record contains useful, reviewable demonstration evidence; it is not a rating of product output quality.
Inputs and weights: Working demo URL 45%; video walkthrough 25%; clear description of what launched 10%; audience 10%; editorial assessment 10%.
Evidence inputs: Demo and video URLs plus the reviewed launch description, audience, and editorial notes.
Creator-story fit
Whether a launch has enough demonstrable evidence and audience relevance for a useful creator story; it does not predict views or guarantee coverage.
Inputs and weights: Demo evidence 25%; visual creator category 15%; audience 15%; editorial assessment 15%; traction evidence 10%; pricing clarity 10%; API or open-weight evidence 10%.
Evidence inputs: Reviewed demo, category, audience, editorial, traction, pricing, API, and open-weight fields.
- Scale
- 0.0–10.0. A present qualifying input receives its published weight; a missing input receives zero. Scores are rounded to one decimal.
- Assigned by
- Suggested by the deterministic field-completeness helper and assigned or approved by a Kingy editorial reviewer.
- Rubric and check date
- Rubric version P0-2026-08-10. The record’s “Last verified” date is the score check date. Checked: 2026-08-07.
- Confidence and missing data
- Confidence depends on source completeness. “Not scored yet” means no reviewed value; “Needs review” means the value or score set failed validation.
- Freshness
- Recalculate after a material launch, source, demo, pricing, audience, or traction change and during the record freshness review.
- Disputes
- Use “Suggest a correction” on the record and cite the relevant evidence. Commercial relationships cannot buy or alter a score.
LFM2.5-2.6B is a credible option for high-volume, privacy-sensitive local agents where compact size and tool use matter more than frontier reasoning. Treat it as…
DeepSeek V4-Flash-0731 launches in API beta with native Codex support
DeepSeek released DeepSeek-V4-Flash-0731 as the official V4-Flash API public beta. The checkpoint keeps the preview model’s architecture and size but adds new post-training, native Responses API support, and…
Worth testing for cost-sensitive coding-agent workloads because it combines native Codex support, a one-million-token context window, very low API pricing and MIT-licensed weights. Treat…
GitHub Copilot cloud agent for Linear reaches general availability
GitHub made its Copilot cloud agent integration for Linear generally available, allowing teams to assign Linear issues to an asynchronous coding agent.
Copilot cloud agent for Linear addresses a concrete need: The integration moves agent assignment into an issue tracker that many software teams already use,…
OpenAI Presence managed enterprise agents
Each deployment begins with a defined job and limited access to the knowledge and systems required for that job. Companies set policies, approved actions, and escalation conditions. Simulations…
OpenAI Presence addresses a concrete need: The launch packages agent design, deployment controls, operational evaluation, and post-launch improvement into one managed enterprise offering rather…
Humalike social behavior plugin for Hermes Agent
The open plugin connects Hermes to Humalike’s behavioral APIs. It can decide when the agent should join a conversation, rewrite replies to match a group’s style, build a…
Humalike x Hermes addresses a concrete need: Agent quality in group settings depends on timing, restraint, and social context as well as answer quality.…
Rex governed agents for order-to-cash work
Rex launched a set of governed AI agents for collections, customer-portal work, accounts-receivable inboxes, cash application, and dispute handling.
Rex addresses a concrete need: Order-to-cash work crosses inboxes, finance systems, and customer portals, making it a useful test of whether agentic software can…
Creed portable Markdown context for AI agents
Creed launched a portable Markdown-based context file that gives supported AI agents a user-controlled record of preferences, projects, and working style.
Creed addresses a concrete need: Users increasingly move among several assistants, while the context each one learns usually stays trapped in a product. The…
Deck AI chief of staff with a delegated inbox
Deck launched an AI chief of staff with its own inbox, letting a user copy selected email threads, assign follow-up work, and schedule recurring routines without granting full…
Deck addresses a concrete need: Giving an assistant its own inbox offers a narrower permission model than granting access to an entire mailbox, while…
Lunen enterprise agent orchestration layer
Lunen.ai launched an early-access enterprise orchestration layer for defining agents in plain language and governing their tools, data access, approvals, schedules, and audit trails.
Lunen.ai addresses a concrete need: Enterprises experimenting with agents need controls at the action layer, not only prompt guidelines. The main limitation is this:…
Fuzzy AI relationship-first sales workspace
Fuzzy AI launched a relationship-first sales workspace that combines prospect research, LinkedIn engagement, personalized outreach, sequencing, and human-reviewed replies.
Fuzzy AI addresses a concrete need: Fuzzy reflects a shift from high-volume AI prospecting toward systems that try to coordinate research, public engagement, and…