Bushmaster

[Bushmaster @Bushmaster18523]

8/

We’re still early, but I suspect “task state” will eventually matter as much as prompts do today.

[Bushmaster @Bushmaster18523]

1/

Over the last few weeks we've seen:

- Goals
- Dynamic Workflows
- Background Agents
- Chief-of-Staff patterns
- Context compression research
- Agent memory systems

The common theme isn't execution.

It's continuity.

[Bushmaster @Bushmaster18523]

7/

My current bet:

Execution will increasingly be owned by vendors.

The continuity layer may become a portable artifact that survives tools, sessions, models, and orchestrators.

[Bushmaster @Bushmaster18523]

@nickbaumann_ Interesting. The CoS thread becomes a durable working memory layer. I wonder whether the long-term artifact ends up being the thread itself or a separate task-state object that survives thread changes, compaction, and tool switches.

[Bushmaster @Bushmaster18523]

@_catwu Dynamic workflows solve execution at scale.

I wonder if we'll eventually need a task-passport artifact alongside them: objectives, decisions, evidence, dead ends, and next actions that survive handoffs between agents, sessions.

Execution scales. Continuity still needs a home

[Bushmaster @Bushmaster18523]

@omarsar0 /goal + durable Task Passport feels like a strong combo for long-running agent work.
Execution is one side; preserving verified task state across sessions/tool switches is the other.
https://t.co/0liUtdhw3L

[Bushmaster @Bushmaster18523]

@DanielMiessler It makes me think workflow execution and workflow continuity are becoming separate layers.

Vendors will own execution/orchestration.
Projects like Agentpack explore the durable continuity side:
https://t.co/0liUtdhw3L

Portable task state, checkpoints, evidence, handoffs.

[Bushmaster @Bushmaster18523]

@Saboo_Shubham_ Check, slightly different layer:
https://t.co/0liUtdhw3L⁠
Focused on durable task state across compaction, new chats, tool switches, and handoffs - preserving decisions, checkpoints, dead ends, and evidence instead of just memory.

[Bushmaster @Bushmaster18523]

@heyblake Built an open-source repo-native continuity layer for coding agents:
https://t.co/0liUtdhw3L⁠
Focused on durable task state across compaction, new chats, tool switches, and handoffs - preserving decisions, checkpoints, dead ends, and evidence instead of just 'memory'.

[Bushmaster @Bushmaster18523]

@0xCodez Check. Built an open-source repo-native continuity layer for coding agents:
https://t.co/0liUtdhw3L⁠
Focused on durable task state across compaction, new chats, tool switches, and handoffs - preserving decisions, checkpoints, dead ends, and evidence instead of just 'memory'.

[Bushmaster @Bushmaster18523]

@rauchg Built an open-source repo-native continuity layer for coding agents:
https://t.co/0liUtdhw3L⁠
Focused on durable task state across compaction, new chats, tool switches, and handoffs - preserving decisions, checkpoints, dead ends, and evidence instead of just 'memory'.

[Bushmaster @Bushmaster18523]

@_vmlops Interesting direction. I’ve been exploring the workflow continuity side of this problem: preserving decisions, checkpoints, dead ends, and resumable task state across compaction/new sessions/tool switching:

https://t.co/0liUtdhw3L

[Bushmaster @Bushmaster18523]

@daniel_mac8 Interesting direction. I’ve been exploring durable task continuity for coding agents across compaction/new chats/tool switches:

https://t.co/0liUtdhw3L

Instead of just “memory”, the focus is preserving decisions, dead ends, checkpoints, evidence, and resumable task state.

[Alfin @AlfinCodes]

Minimum requirements for Linux

[Akshay 🚀 @akshay_pachaar]

code as agent harness.

a 102-page survey from Stanford, Meta, and UIUC on agent harnesses.

the paper argues that code is no longer just the thing agents produce. it’s the medium through which they reason, act, and represent their environment.

it calls this “code as agent harness” and covers three layers: code as the interface between agents and their tasks; the mechanisms that keep agents reliable over long-horizon execution (planning, memory, tool use, verification); and how multi-agent systems coordinate through shared code artifacts.

core findings:

the paper introduces “evolution agents” that treat the harness itself as the optimization target. they collect telemetry, diagnose failures, propose infrastructure changes, and promote only mutations that pass regression. the harness improves itself.

in multi-agent systems, topology complexity inversely correlates with infrastructure quality. teams with better shared state use simpler coordination. teams without it build increasingly elaborate workarounds.

finally, the paper concludes that future agent systems need four properties:

- executable
- inspectable
- stateful
- governed

read more: https://t.co/mRMB58QduK

i also published this deep dive (article) on agent harness engineering, covering the orchestration loop, tools, memory, context management, and everything else that transforms a stateless LLM into a capable agent.

the article is quoted below.