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.
The Engineer · Build desk

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
Claim ledger
Ranked by verification strength, evidence, and original report placement.
- [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.
- [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.
- [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.
- [4]
The article walks through six specifications it says together make the pieces of an agentic system portable: the Model Context Protocol (MCP), Agent Skills, Agent2Agent (A2A), the Open Agent Profile (OAP), the Agentic Graph Specification (AGS), and the Agent Approval Interchange Specification (AAIS).
- [5]
The article says three of the six have institutional weight behind them: MCP and A2A both live at the Linux Foundation, and Agent Skills is stewarded through the Agentic AI Foundation.
- [6]
The author discloses that he authored the last three of the six specifications (OAP, AGS and AAIS) and that he works at Dremio, which ships an MCP Server as part of its platform.
- [7]
The author says the other three specifications are young, that he wrote them, and that readers should weigh his enthusiasm accordingly; he also says he will note where each standard is young, unproven, or the wrong tool.
- [8]
The article describes learned state as the facts an agent picked up about your systems that make it useful on day 180 in a way it was not on day one.
- [9]
The article says the model layer was settled by competition: a request can be routed to a frontier model from any of a half dozen providers, or an open-weight model run on your own hardware, and switched between in an afternoon.
- [10]
The article draws the parallel to data infrastructure, where the fix was specifications rather than a better warehouse: Parquet for files, Iceberg for tables, Polaris for catalogs, with Iceberg largely settling the table-format fight by making tables an open specification any engine can read.
- [11]
The article describes an agent as a configuration scattered across one vendor's product: a system prompt in one screen, tool grants in another, accumulated context in a proprietary store, and approval rules in a settings page nobody remembers configuring.
- [12]
The article argues the six specifications are not competitors: each answers a different question about an agentic system, and a complete system needs an answer to all six.
- [13]
Three of the six specifications named have an institutional steward and three share a single author, so half the coverage of the six-artifact harness stack rests on documents with one author.
- [14]
The article states that none of the accumulated harness state has anything to do with which model you use, and that absent standards all of it lives in one product's shape.
- [15]
The article credits Agent Skills with adoption across directly competing vendors.
- [16]
The article says every team that gets serious about AI agents hits the same portability wall, usually around month three.
- [17]
The article says switching harnesses means reconstructing the accumulated configuration from memory, which is expensive enough that most teams never do it, and calls this lock-in that is not a contract but a moat made of your own accumulated work.
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Sources
1 independent publisher whose own reporting we read for this story.
- dev.toOpen Standards for Agentic Harnesses
1 article · August 31, 2026
Topics and entities
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Topics
- Agent harness portabilityFollow
- Open specification governanceFollow
- Agent tooling interoperabilityFollow
- AI Vendor Lock-In and Switching CostsFollow
Entities
- Agent Approval Interchange SpecificationFollow
- Open Agent ProfileFollow
- Model Context ProtocolFollow
- Linux FoundationFollow
- Agent2AgentFollow
- gooseFollow
- DremioFollow
- OpenCodeFollow
- Apache ParquetFollow
- Claude CodeFollow
- Alex MercedFollow
- Apache IcebergFollow
- Agentic Graph SpecificationFollow
- Apache PolarisFollow
- Codex CLIFollow
- Agent SkillsFollow
- Agentic AI FoundationFollow
- CursorFollow
- AnthropicFollow