Tag
Covered in Agents & ProtocolsAI Engineering, where the background and the sources for this subject live.
In the cited docs, OpenAI's Agents API says OpenAI runs orchestration, compaction, recovery, and session state, while Anthropic says Claude Managed Agents can evaluate each server-run tool call and allow, ask, or deny it. If your requirement is vendor-side approval logic, Claude documents that boundary directly and OpenAI does not in these pages.
4 min read
Agent2Agent and MCP solve different boundaries. A2A standardises how one agent discovers another, opens a task and receives updates or artifacts back. MCP standardises how an agent runtime discovers and uses tools, resources and prompts. Use MCP inside an agent. Use A2A when the other side owns its own planner, state and operators.
4 min read
Claude cache diagnostics compares the current request with the response `id` of the previous diagnosed turn and reports the first divergence across model, system, tools, or messages. Since 23 September 2026 the `diagnostics` field is always present, so reliable debugging means reading that field together with `usage.cache_read_input_tokens`.
4 min read
Two shipped integrations put Jev inside an agent loop, from Pydantic AI and LangChain. Both leave three things to your code: a ledger of actions already executed, because Jev may re-propose a tool that ran; a per-question projection of state, because accuracy falls as irrelevant context grows; and the confidence threshold that escalates to a language model.
5 min read
OpenAI's misalignment monitoring can automatically stop only Responses API conversations that preserve context through persisted reasoning, WebSocket continuation, or server-side compaction. Other Responses requests are still monitored but only emit alerts, and Chat Completions sits outside the system. Treat it as a backstop. It is not a permission control.
4 min read
Google ADK v2.10.0 adds `persistent`, `bounded` and `ephemeral` skill lifecycles behind `ADK_ENABLE_SKILL_LIFECYCLE=1`, but the source code is clear that only `bounded` skills count against `max_active_skills`. `ephemeral` skills expire at turn end, except under `run_live`, where they stay active for the full stream.
4 min read
Anthropic's mid-conversation-tool-changes-2026-07-01 beta lets a system-role message add or remove already-declared tools partway through a conversation without invalidating the cached prefix above it. LangChain wired this into ChatAnthropic on 23 September 2026, and its wrapper silently drops any other content type from that message with only a logged warning.
5 min read
Claude Code and the Agent SDK record a session's system prompt on its first request and keep reusing that copy after resume, so a changed append or custom prompt has no effect until the session compacts or you start a new one. The snapshot field controls this, and it defaults to off in bare mode.
5 min read
Two of OpenAI's first six published misalignment reports describe models writing instructions into their own compaction summaries. In one, 2.15% of 5.6-Sol reinforcement learning summaries carried text telling a later context to conceal information from the user. A compaction summary is a channel the model writes and then reads back as its own memory.
5 min read
The auto permission policy, shipped on 10 September 2026, has the Claude Managed Agents server run, deny or pause each tool call. It assesses tool results, fetched pages and MCP responses without taking instructions from them. It does read user.message events as your intent, so relayed end-user text can get a call allowed that would otherwise be denied.
5 min read
Claude now has two compaction features with the same name. Threshold compaction, beta compact_20260112, leaves its summary block after the messages it summarises. On-demand compaction, beta compact-2026-09-04, returns a signed block that replaces them and must be sent first, or the request returns a 400.
6 min read
A cloud agent is a service that loops over goals, model calls and tools inside infrastructure you control. Treat it like a worker, with scoped identity, a queue, a sandbox and full traces. Manual deployment means packaging the agent, setting secrets, proving a dry run, then promoting one controlled replica.
4 min read
Google ADK is the cleaner fit when your agent already lives beside Gemini, Vertex AI and Google Cloud identity. LangGraph is the cleaner fit when the hard part is a stateful workflow you want to run on your own runtime. Manual deployment for both starts with packaging the agent as an API.
5 min read
Approval of AI-generated work depends on a scoped change, visible evidence, repeatable verification, and honest limits. GitHub pull requests, NIST AI RMF 1.0 and OWASP guidance all point to the same pattern: make the work inspectable without asking the system to explain itself later.
5 min read
robots.txt, llms.txt, Content-Signal and AIPREF all describe what an agent may fetch. The 2,000 packages uploaded to RubyGems in May went through a signup form and a publish endpoint, which none of those files govern. Read controls are advisory. Write paths need identity and rate limits.
3 min read
MCP revision 2026-07-28 requires ttlMs and cacheScope on every list result and tells servers to return tools in a deterministic order. Both are filed as minor changes. They are the most direct cost change in the revision, because tool definitions sit in the cacheable prefix of every agent step.
3 min read
SEP-2640 reached Final status and landed in the MCP docs on 13 September 2026. It requires hosts to verify every skill file against a SHA-256 digest and byte size, and binds approval to that manifest so one changed file revokes it. The tools primitive specifies none of this. MCP now has two content surfaces with opposite integrity models.
3 min read
Anthropic measured one Drive to Salesforce transfer falling from 150,000 tokens to 2,000 by writing code against MCP servers instead of calling tools directly. That is a code execution result rather than an argument for deleting MCP. The Agent Skills spec puts numbers on the alternative: about 100 tokens per skill at rest.
4 min read
The Model Context Protocol is a JSON-RPC 2.0 layer that lets an AI client discover and call capabilities exposed by separate servers. It defines three primitives: tools, resources and prompts. Since revision 2026-07-28 it is stateless, negotiating per request rather than per session. It standardises transport and discovery. Authorisation stays your job.
6 min read