In late September 2025, Anthropic unveiled Claude Sonnet 4.5, its most advanced model yet, and a provocative statement accompanied the release: this is “the best coding model in the world,” the “strongest model for building complex agents,” and the premier AI for interacting with computers.
Those claims demand scrutiny. For organizations considering agentic AI, Sonnet 4.5 is arguably the boldest contender yet. Below, I review what makes this model stand out, explore the implications for data systems and engineering, and offer guidance for teams evaluating it.

What’s New in Sonnet 4.5?

Extended Autonomy

One of the most striking capabilities Anthropic highlights is that Sonnet 4.5 can sustain continuous operation for up to 30 hours on complex, multi-step tasks. That is a marked leap from prior versions. Internal tests included building a web app from scratch and maintaining context and purpose well past what previous versions could manage.

Coding & Computer Use Improvements

Anthropic reports that Sonnet 4.5 outperforms earlier versions in coding, tool orchestration, context persistence, and system navigation.

For instance:

• It executes file operations, spreadsheet manipulation, document handling directly within the conversation interface.

• Checkpoints are introduced in Claude Code, letting users roll back to previous states.
• Stronger alignment and safety guardrails aim to reduce undesirable behaviours (sycophancy, deception, power-seeking) during long agentic runs.

Claude Sonnet 4.5 is now in public preview as the model powering the Copilot coding agent. If you’re a Copilot Pro / Pro+ user, the coding agent will use Sonnet 4.5 by default; for Copilot Business / Enterprise, it must be enabled via policy.

Benchmark Gains

Sonnet 4.5 reports strong benchmark performance. On OSWorld, which tests AI models in real-world computer tasks, it scores 61.4% (up from 42.2% for earlier Sonnet 4). It also reportedly leads on SWE-bench Verified, a coding benchmark, surpassing some recent rivals. Nonetheless, benchmarks are just data points, not guarantees.

Under the Hood: Architectural & Training Shifts (What We Can Infer)

Anthropic has not published a full technical architecture paper as of this writing. But from public clues and model behavior, the following shifts seem likely:

Anthropic made Sonnet 4.5 available via Amazon Bedrock, and in that environment, it introduces features tailored for long-running agentic tasks. For example:

• Automatic cleanup of tool interaction history during long conversations, to reduce token bloat and maintain responsiveness.

• A memory tool that lets Claude store and consult data outside the immediate context window, improving continuity across sessions.

What This Means for Data Engineering & Infrastructure

Agentic AI at this level has real consequences for how data systems, pipelines, and observability are built. Here are key considerations for technical leaders:

1. Pipelines Become Semi-Autonomous Workflows

Sonnet 4.5 begins to make it plausible to entrust AI agents with monitoring, repairing, or evolving data pipelines on their own. For example:

• Detecting and recovering from ingestion failures.

• Adjusting transformations when schema drift is detected.

• Auto-generating monitoring or alerting rules based on usage.

To safely adopt this, teams will need guard rails, overseers, and escalation paths an AI agent should not go rogue.

2. Observability Expands to AI Metrics

Traditional telemetry (CPU, memory, latency) is insufficient. With long-running agents, you need to track:

• Drift: How far has the model deviated from original goals?

• Error accumulation: How many tool invocations failed or needed retries?

• Memory degradation: Does the agent begin forgetting earlier steps or context?

• Safety events: Signs of hallucination, unsafe commands, or misalignment.

Dashboards must evolve to include “AI health” channels.

3. Reliability & Fail-safe Design

An agent that can act for 30 hours is also capable of compounding mistakes for 30 hours. Strategies to mitigate risk include:

• Watchdog agents that periodically inspect the primary agent’s state.

• Checkpointing so you can roll back to known safe states.

• Human-in-the-loop thresholds for high-impact decisions.

• Simulation and dry-run modes to validate actions before applying.

4. Governance, Audits & Traceability

Enterprises will demand audit logs of every agent action, versioning of decision logic, and traceability from actions back to prompts and policy. For regulated domains, this is non-negotiable.

Limitations & Cautions

No model is flawless and Sonnet 4.5 comes with caveats you should weigh:

• Benchmark vs. Real World: Benchmarks can be overfit or contaminated. What reads well in lab settings may reveal edge-case failures in production.

• Code Quality & Security Gaps: A recent study evaluating AI-generated code across models revealed that functional correctness does not always imply security or maintainability. Hard-coded secrets, code smells, and subtle vulnerabilities appeared in outputs.

• Autonomy Without Oversight Is Dangerous: Agents can drift objectives, misinterpret ambiguous instructions, or repeat bad behaviors in long loops.

• Cost & Efficiency: Running a model with sustained context, tool orchestration, and guardrails may incur higher compute and latency overhead.

• Competition & Pace: The AI frontier is moving fast. Sonnet 4.5 may reign briefly before challengers emerge (e.g. future versions from OpenAI, Google, others).
  
  

  Verdict & Advice for Adopters

  Claude Sonnet 4.5 is not just an upgrade—it is one of the clearest signals yet that Anthropic intends Claude models not for simple chat, but as foundation models for autonomous systems.

  For engineering teams and enterprises:

  • Experiment early, but prudently: Start with low-risk tasks (e.g. monitoring, reporting) and layer in autonomy over time.

  • Build scaffolding now: Create the observability, rollback, and audit infrastructure before you let agents touch critical systems.

  • Combine AI with human oversight: Especially in early adoption, maintain escalation channels.

  • Benchmark internally: Don’t rely only on public metrics—test Sonnet 4.5 against your domain workloads.

  • Stay alert to updates: Monitor safety reports, usage behavior, and competitor releases.

  If you’re evaluating Sonnet 4.5 for your own workflows, start small: test it on contained tasks, build observability scaffolding early, and benchmark it against your domain needs. The companies that prepare now will be the ones best positioned when agentic AI becomes the new baseline.
  What opportunities do you see in AI agents that can run for 30 hours without stopping and are you concere?