Claude Opus 4.6: How new AI agents and a 1M‑token context change AI for business

TL;DR: Anthropic’s Claude Opus 4.6 pushes LLMs toward autonomous, end‑to‑end work on high‑value enterprise tasks. Key advances: stronger agentic planning (parallel subagents), a beta 1M‑token long‑context window, and productivity integrations like PowerPoint that respect templates. Early vendor tests report measurable lifts in legal and multi‑source reasoning, but most headline features are in preview and require staged pilots, observability, and governance before running mission‑critical workflows.

What’s new — three technical advances that matter

1) Agentic planning and AI agents in enterprise

Agentic (or “agent‑style”) behavior means the model can plan, break a job into subtasks, and act on those subtasks — not just respond to single prompts. A subagent is a specialized, temporary worker the model spawns to handle one piece of the job. Opus 4.6 introduces more robust planning and the ability to run subtasks in parallel, coordinating results into a final deliverable.

“Opus 4.6 is a major step for agentic planning — it decomposes complex tasks, runs subtasks in parallel, and identifies blockers precisely.” — Michele Catasta, Replit

Think of it like a small project team: a researcher pulls sources, an analyst builds a model, and a writer drafts a memo — all coordinated automatically by the model, with humans able to step in.

2) 1M‑token context: a “single‑page memory” for big files

A context window (or context size) is how much text the model can keep “in view” during reasoning. The new beta 1M‑token context lets Opus hold entire contracts, long codebases, or dossiers without chopping them into many prompts. In plain terms: instead of flipping through dozens of snippets, Opus can keep the whole document on a single virtual page.

Approximate scale: a typical single‑spaced page of legal text is roughly 400–800 tokens, so a 100‑page contract can fall in the tens of thousands of tokens. A 1M‑token window handles very large projects — entire repositories or long litigation files — with much less prompt engineering overhead.

3) Productivity integrations that respect workflow and branding

PowerPoint integration in research preview can read slide masters and stay on‑template, building decks inside the environment rather than dumping content into a generic editor. For teams that require branding compliance and slide structure, that’s a practical step toward using LLMs as true productivity tools rather than copy‑paste helpers.

Why this matters for business leaders

These capabilities change where and how LLMs can be useful. Instead of just generating copy or answering questions, the model aims to perform the triage, analysis, and production steps of knowledge work with fewer human rewrites. That matters for:

• Legal teams producing memos or discovery summaries
• Finance groups building or auditing complex models
• Engineering teams analyzing large codebases
• Marketing and sales teams creating compliant slide decks and proposals

Example vignette (illustrative): A litigation team feeds a case’s pleadings, depositions, and contracts into the model, asks for a litigation memo, and receives a draft that identifies key issues, cites relevant passages, and suggests next steps. Where a full manual memo might take 2–3 days, a first draft could be ready within a business day — with the human lawyer focused on verification and strategy rather than sourcing.

Performance signals — what the numbers say (and what they don’t)

Vendor tests show meaningful gains, but treat numbers as directional and vendor‑reported unless independently audited.

• Box (Yashodha Bhavnani) reported roughly a 10% lift on multi‑source, high‑reasoning tasks — moving an internal benchmark from ~58% to ~68% accuracy on complex problems.
• Harvey (Niko Grupen) ran Opus on the BigLaw Bench and reported a 90.2% score, with 40% perfect cases and 84% above 0.8 on their scale.

These results highlight improved legal and technical reasoning compared with previous Opus versions, but they don’t replace domain‑specific validation. The size and methodology of these tests vary; leaders should ask vendors for test set size, question types, and whether datasets overlap with training data before drawing operational conclusions.

Availability and feature status

• Accessible via claude.ai, Anthropic’s API, and major cloud partners.
• Token pricing unchanged from the prior Opus release.
• PowerPoint integration, 1M‑token context, and agent teams are in research preview or beta — not yet full GA with enterprise SLAs.
• Opus 4.6 also aims to reduce interruptions reported by some Opus 4.5 users related to internal compaction sequences (the model’s memory‑management processes).

Key operational questions for executives

• Can this run mission‑critical workflows?
  Partly. The tech is promising, but readiness depends on domain validation, observability, and control points you build around the model.
• Will agent teams make work faster or more opaque?
  They can speed up multi‑step tasks by parallelizing subtasks — but transparency is mandatory. Log intermediate outputs, prompt history, and decision paths so humans can audit and intervene.
• What about cost and latency for 1M‑token runs?
  Expect higher compute and potential latency. Pilot with representative inputs and evaluate caching, selective context, and hybrid approaches before scaling.

How to pilot Opus 4.6 — an 8‑step playbook

1. Pick 1–2 high‑value, complex workflows (e.g., contract review, financial model QA).
2. Define success metrics: accuracy rate, time saved, hallucination rate, cost per deliverable.
3. Prepare canonical inputs: representative contracts, a slice of a codebase, slide templates.
4. Instrument logging: prompt history, intermediate agent outputs, timestamps, compute usage.
5. Run controlled tests with a human‑in‑loop reviewer at every outcome.
6. Measure latency and cost using 1M‑token contexts and compare selective‑context strategies.
7. Test interruption and intervention: can humans stop subagents, correct plans, or reroute work?
8. Review results, iterate prompts, and expand to additional workflows only when you meet targets.

Risks & mitigations

• Hallucination: Red‑team outputs, require citations, and enforce human verification for critical facts.
• Data leakage: Use strict access controls, encryption, and tenancy separation for sensitive inputs.
• Opaque reasoning: Log intermediate agent states and require explainability checkpoints before finalization.
• Model drift and reliability: Monitor performance over time and revalidate with fresh datasets periodically.
• Cost overruns: Track compute per workflow and implement fallbacks (selective context, caching).

FAQ

Is Opus 4.6 a ChatGPT competitor?
Yes — it’s positioned as a frontier model competing with other large models in enterprise settings. The important differentiators here are agent orchestration and the very large context window designed for long‑horizon business tasks.

Does the 1M‑token context mean I should always feed everything into the model?
Not necessarily. Use full context when you need holistic visibility (entire contract sets, full codebase slices). For routine tasks, selective context with smart retrieval will often be more cost‑efficient.

How long should a pilot run?
A practical pilot is 4–8 weeks: enough time to validate quality, measure cost/latency, and test governance controls.

Three things to measure this week if you’re piloting

• Accuracy on representative tasks (baseline vs. Opus outputs).
• Human review time saved per deliverable.
• End‑to‑end latency and compute cost for 1M‑token runs versus selective context runs.

Anthropic’s Opus 4.6 is another clear step toward LLMs acting as coordinated AI agents for AI automation and AI for business. The upside is faster deliverables and fewer handoffs; the hard work is designing observability, governance, and staged rollouts so those gains are reliable and auditable. Start small, measure everything, and treat autonomy as a capability you enable with controls — not as an immediate replacement for subject‑matter expertise.