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Six specifications decide whether an agent can move off the harness it was built on

Lock-in migrated from the model to the runtime that holds your tool grants, approval rules and learned state. Six specifications aim to make that runtime's contents portable, and half of them are one person's drafts.

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What happened

  • A dev.to walkthrough names Claude Code, OpenAI's Codex CLI, Cursor's agent mode, Goose and OpenCode as harnesses, and argues the harness is the layer that quietly inherited the lock-in.
  • It inventories what accumulates inside one after six months: agent definitions, tool connections, procedural knowledge, work plans, approval rules, and learned state about your systems.
  • Six specifications are offered as the fix: MCP, Agent Skills, A2A, the Open Agent Profile, the Agentic Graph Specification and the Agent Approval Interchange Specification.
  • Three of the six have institutional stewards, with MCP and A2A now at the Linux Foundation and Agent Skills stewarded through the Agentic AI Foundation.

Why it matters

  • What it limits constraint Model interchangeability buys nothing at this layer: with half a dozen providers a router away, the artifacts holding the actual work are model-independent, so a swappable model leaves the migration bill exactly where it was.
  • Who has to decide decision Harness selection turns into an export question, because the fallback is reconstructing six months of configuration from memory at a price most teams decide not to pay.
  • What it costs cost Writing an exporter against a spec whose only steward is its author means you own the churn, and there is no neutral body to appeal to when the document moves under you.
  • Who's exposed exposure The approval record is the artifact that decides when a human gets asked, and it is the one whose interchange format is least tested, so governance is what quietly degrades in a migration.

The stickiness is in the loop. A harness holds the conversation, dispatches each tool call, checks it against a permission rule, manages context, and turns the model's text into work [1]. Every one of those steps writes state, and the state lands in different places: the system prompt on one screen, the tool grants on another, the context in a proprietary store, the approval rules in what the piece calls a settings page nobody remembers configuring [11]. That last location I believe without needing evidence.

What a specification does here is the trick Parquet did for files, Iceberg for tables and Polaris for catalogs: it turns a proprietary internal structure into a document any conforming system reads [10]. The trick has a boundary. The document carries the rule; the enforcement point stays inside the loop. Two harnesses can parse the same approval artifact and still disagree about which call trips it, because a spec constrains the file and not the runtime reading it. So the useful test for each of the six is a second implementation, preferably an unfriendly one, that reads the artifact and produces the same grants and the same refusals. And a spec is only half a migration path. The other half is the incumbent product choosing to emit the artifact, which no document can compel.

By that test the six are unevenly placed, and the source is explicit that they are not competitors, each answering a different question, with a complete system needing an answer to all six [12]. Three have outside stewards [5]. The other three, OAP, AGS and AAIS, were written by the author, who also works at Dremio, which ships an MCP Server as part of its platform [6]. He states this near the top and tells readers to weigh his enthusiasm accordingly [7], which is the right way to publish a spec you wrote. It still means half the coverage of a six-artifact stack rests on drafts with a single author [13], and the approval artifact is in that half.

Timing is what makes this expensive rather than merely irritating. The piece puts the wall at around month three [16] and dates real usefulness to the gap between day one and day 180 [8]. An agent becomes worth moving on the same curve that makes it hard to move.

Agent Skills is the one the source credits with adoption across directly competing vendors [15]. On the evidence supplied, that is the only artifact type here with any claim to having crossed between two runtimes whose owners have no interest in each other's success.

What to watch

  • Whether OAP, AGS or AAIS acquire a steward outside their author's employer, or remain single-author drafts.
  • A demonstrated round trip: an agent definition plus its approval rules exported from one harness and enforced identically by another.
  • Any harness publishing a documented export path for accumulated context and learned state, not just prompts and tool lists.

Clarity's read

What the record supports and how the coverage leans. The claims behind it follow.

Reality

Evidence36
Adoption24
Hype gap+27
Incentives74
Confidence42
Why these scores

Claim ledger

Ranked by verification strength, evidence, and original report placement.

  1. [1]

    The article defines a harness as the runtime around a model: the software that holds the conversation loop, executes tool calls, enforces permissions, manages context, and turns a model's text output into actual work.

    Reported SupportedView cited source
  2. [2]

    The article names Claude Code, OpenAI's Codex CLI, Cursor's agent mode, Goose, OpenCode and internal orchestrators enterprises build on frameworks as harnesses, and calls the harness the layer that quietly inherited the lock-in previously argued over at the storage/table-format and model layers.

    Reported SupportedView cited source
  3. [3]

    The article lists what accumulates inside a harness after six months of real use: agent definitions (roles, instructions, personas), tool connections, procedural knowledge, work plans, approval rules, and learned state.

    Reported SupportedView cited source

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Sources

1 independent publisher whose own reporting we read for this story.

  1. dev.to

    1 article · August 31, 2026

    Open Standards for Agentic Harnesses

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