The real comparison axis for AI agents is now whether they can carry work forward

1. Project and session continuity

The first requirement is a durable unit of work that is wider than a single chat turn. ChatGPT Projects are described as workspaces for long-running efforts and repeatable workflows. Vertex AI Agent Engine Sessions play a similar role, keeping the history and progress of an ongoing interaction. Without this layer, the agent has to restart from scratch each time.

2. Execution continuity

The next layer is the ability to carry execution forward, not just remember text. OpenAI’s SandboxAgent model supports start, stop, and resume patterns. Anthropic’s Managed Agents architecture moves the session log outside the harness and sandbox so work can continue even when a container fails. AWS AgentCore Runtime similarly treats asynchronous agents and true session isolation as core infrastructure.

3. Artifact continuity

Long-running work depends on files and intermediate outputs, not only on final responses. Google ADK separates Artifacts from session state so larger generated outputs can be persisted, versioned, and shared. Anthropic also makes handoff artifacts and file-based communication part of the story for multi-session work. Without an artifact layer, agents struggle to hand off unfinished work safely.

4. Operational continuity

Carrying work forward also means being able to observe and judge it while it is still operating. AWS AgentCore Observability and Evaluations make session count, latency, errors, traces, and continuous scoring of production traffic visible. That means “ongoing work” is no longer only a prompt-engineering issue. It is also an operational design issue.

OpenAI ties together the user-facing continuity surfaces

Projects hold recurring work context. Tasks schedule work even when the user is offline. Apps add search, sync, and action surfaces across external tools. Codex now adds background computer use, ongoing and repeatable work, memory, and automations. OpenAI’s distinctive move is that the continuity story is no longer only in developer APIs. It is visible in end-user products too.

Anthropic makes the architectural principles explicit

Managed Agents defines the session as an append-only log, separates the brain from the hands, and treats the sandbox as an interchangeable execution surface. The Claude Agent SDK also exposes session resume directly, while the code execution tool makes pause-and-continue and isolated containers concrete. Anthropic’s strength is the clarity of its design language around long-running work.

Google separates the state contract cleanly

Vertex AI Agent Engine Sessions separates events, state, and memory, while ADK treats state as the session scratchpad and artifacts as the proper home for larger outputs. Session TTL and listing also appear in the public docs, which makes lifecycle control part of the documented contract for ongoing work.

AWS emphasizes production operation and governance

AgentCore Runtime centers asynchronous agents and session isolation, Observability exposes the operational path of each run, and Evaluations scores agents continuously against live and test traffic. AWS’s strongest message is that a durable agent is not only one that can continue working, but one that can be operated, measured, and governed while it continues working.

Scheduling is not full automation

Tasks and automations make it possible for an agent to keep moving while the user is away. But that does not mean every judgment should be delegated. The real value comes when the agent can restart on a schedule, move the work forward, and return the right decision points to a human at the right moment.

Memory is not a substitute for correctness

Codex memory and Google’s cross-session memory strengthen continuity, but they also preserve old assumptions. The important capability is not only that the system can remember, but that it can decide what to reuse and what to discard.

Resume is also a recoverability problem

OpenAI’s snapshotting and rehydration plus Anthropic’s durable session log show that continuity is not only about holding conversation context. It is also about whether the system can recover from container loss, session interruption, or partial failure without restarting the whole task.

Continuity matters most when it is paired with auditability

AWS puts traces, latency, error rates, and session count at the center for a reason. The longer an agent works, the more important it becomes to reconstruct what happened after the fact.

Weekly research and reporting

• Keep prior notes, files, and instructions inside a project.
• Use tasks or automations to run on a schedule and pick up from prior context.
• Pull fresh context from Slack, Drive, Notion, or other connected apps only when needed.

Multi-day implementation or validation work

• Resume a sandbox or container with its working state intact.
• Keep artifacts and session logs available for review in the middle of the run.
• Avoid re-explaining the full task from scratch every time work pauses.

Recurring SaaS follow-up work

• Use apps or tools only when external context or write actions are needed.
• Combine schedules, memory, and external data to keep follow-up loops alive.
• Treat confirmations and permission boundaries as part of the workflow, not as optional extras.

Internal operations with audit requirements

• Session count, latency, traces, and errors make it easier to take operating responsibility.
• Continuous evaluation helps surface drift, not just headline success rates.
• The ability to resume work and the ability to audit it are becoming equally important.

For knowledge work, project surfaces and app connectivity matter first

In research, planning, document review, and follow-up work, durable compute is often less important than projects, apps, memory, and scheduled tasks. Quality depends on where prior context is stored and how the next run reconnects to fresh information.

For development work, sandboxes and artifacts matter more

Multi-day implementation, validation, and migration work requires more than session history. It needs persistent files, reproducible outputs, and resumable execution environments. In this segment, resumable sandboxes and reusable containers become a larger differentiator.

For cross-SaaS workflows, identity and approval become the bottleneck

Slack, Gmail, Notion, and internal SaaS follow-up work looks attractive on paper, but the real constraint is often not memory. It is write-path governance. Teams need to decide which actions can be automated and where confirmation gates must remain.

For internal operations, weak observability becomes the first limit

In ops and high-trust internal workflows, the problem is often not whether the agent can keep going. It is whether failures can be found quickly enough. Traces, error breakdowns, session counts, and token usage become part of product selection.

1. Resume success rate

How often the agent can return naturally to the prior task after a stop, container replacement, or session reload without meaningful quality loss.

2. Stale-context incident rate

How often the system continues working from outdated state, memory, or assumptions. This becomes a visible failure mode once work spans multiple days.

3. Artifact handoff quality

Whether intermediate files, generated outputs, and checkpoints remain readable, reusable, and reviewable by either the next run or the next human reviewer.

4. Intervention cost

How often humans need to stop the run, restate context, or redirect the work. Better continuity should reduce the granularity of human supervision.

Product teams need to define the unit of work first

Whether the system is organized around a conversation, a project, or a recurring weekly task changes which continuity surfaces are actually required.

Platform teams should separate state from compute

Managed Agents and the Agents SDK both point toward the same design lesson: durability and recovery become easier when context storage and execution environments are not tightly coupled.

Operations teams should standardize traces and review loops early

An ongoing agent starts to resemble an operational workload more than a chatbot. Monitoring, failure classification, and rerun procedures should be standardized accordingly.

Business teams should start with work that benefits from being carried forward

Recurring research, PR follow-up, unresolved comment tracking, and report drafting are better entry points than trying to automate every high-risk workflow at once.

Scheduling alone does not improve quality

A recurring trigger is useful, but without a clear success definition and review loop, the agent can simply repeat stale work more efficiently.

Persistent context can also preserve bad assumptions

Project memory and session state are powerful, but they can carry old judgments forward too. Work continuity depends as much on forgetting and pruning as on remembering.

External actions still require identity and approval design

As apps and browser actions spread, write permissions, OAuth scopes, workspace controls, and audit trails become part of the continuity contract. Ongoing work cannot ignore access design.

Observability is not a substitute for a good success definition

Seeing traces and metrics is useful, but it does not tell you what “good” means. The longer the work horizon becomes, the more important it is to define task completion and failure boundaries in advance.