OpenAI Launches ChatGPT Agent as Google and Meta Roll Out Major Updates This Week

Surprising fact: 26.6% improved HLE in a recent Deep Research rollout shows how quickly agent tools lift performance for complex tasks.

We explain what this week’s moves mean for Australian brands. Open AI has introduced agentic features like Operator and a Deep Research agent, while o‑series reasoning models are changing how teams think about long tasks.

At the same time, Google’s Gemini API now plugs into existing libraries with three small changes, bringing reasoning controls, streaming and image/audio support to the same stack. We focus on the practical details: where to pilot, which features are production‑ready and what to protect in your customer data and IP.

We keep this short so you can act in good time. If you’re struggling with your Meta rollout or need fast triage, contact hello@defyn.com.au and we’ll prioritise your case.

Key Takeaways

• New agent tools accelerate complex workflows; assess pilots now.
• o‑series reasoning models shift expectations for long‑form work.
• Google’s Gemini compatibility reduces integration friction.
• Protect customer data and IP as features scale to production.
• Contact us at hello@defyn.com.au for urgent Meta or rollout help.

Open AI landscape update: this week’s agent launch and big‑tech moves at a glance

This week clarifies which agent features you can pilot now and which should wait on your roadmap.

Operator launched 23 January 2025 for Pro users in the US as a web automation agent that executes user goals across sites. On 2 February a Deep Research agent hit Pro tiers at $200/month with up to 100 queries and scored 26.6% on the Humanity’s Last Exam benchmark.

These releases signal a move toward more autonomous workflows: better retrieval, planning and auditable steps. We summarise what ships now versus what’s staged so you can plan team time and avoid dead ends.

Practical integration and Gemini compatibility

Google says Gemini models are callable via OpenAI libraries by changing three lines: api_key, base_url and model. That lowers switching costs and enables hybrid stacks.

“Use reasoning_effort or Gemini’s thinking_budget — not both — to tune cost, accuracy and latency.”

• Key wins: consolidate orchestration, cut vendor lock‑in and sharpen negotiation leverage.
• Migration tip: pilot a single service endpoint behind a feature flag, route by SLA.

If Meta changes are disrupting your deploy cadence, contact hello@defyn.com.au and we’ll stabilise your release plan quickly.

Open AI

We map how OpenAI’s history and funding shape what Australian teams should expect from future artificial general intelligence projects.

Founded in December 2015 in San Francisco, the organisation was built by Sam Altman, Elon Musk, Ilya Sutskever, Greg Brockman and others with a mission to develop safe and beneficial AGI.

Microsoft’s US$13 billion investment and Azure compute underpin product scaling. The product line spans ChatGPT, the GPT series, DALL·E, Sora, SearchGPT and the o‑series reasoning models, plus tools such as Operator and Deep Research.

Greg Brockman met yoshua bengio in 2016 while recruiting early researchers, a signal of the research calibre that underpinned early progress.

• What this means for procurement: governance and funding affect delivery speed and roadmap stability.
• Data posture: partnerships with publishers shape capability and licensing risk.
• Time horizons: some models are dependable now; others warrant pilots.

“We recommend treating agentic and reasoning features as staged investments, not immediate full rollouts.”

What is the ChatGPT Agent and how it differs from GPTs and previous models

We describe the agent’s role in orchestrating tasks and how that changes the way teams use models.

The ChatGPT Agent is a task‑oriented orchestration layer. It plans steps, calls tools, and checks outputs. That is unlike simple prompt‑completion flows in earlier model generations.

From GPT‑4 style systems to o1 reasoning

The o1 model (Sept 2024) focuses on enhanced reasoning and better intermediate thought management. GPT‑4‑class systems remain strong for conversational text and shorter tasks.

Agentic capabilities: web automation and controlled outputs

Operator (23 Jan 2025) extends agents to web automation: logging in, navigating sites and completing forms with auditable steps. Deep Research (Feb 2025) supports long‑form desk research and showed 26.6% HLE in early access.

Multimodal fit: images, video and content workflows

DALL·E and Sora map to creative pipelines: DALL·E for image assets, Sora for text‑to‑video. Use agents when tasks need orchestration; call models directly for simple, low‑risk outputs.

“Choose agents for complexity and governance; use direct calls for speed and simple outputs.”

Google and Meta this week: key updates you should care about

We compare vendor controls and translate platform changes into concrete steps you can pilot this time. Gemini models are reachable via OpenAI libraries by switching api_key, base_url and model. Gemini supports thinking budgets and optional thought summaries; those budgets cannot run at the same time as OpenAI’s reasoning_effort.

Gemini thinking vs reasoning_effort

Reasoning levels: OpenAI’s reasoning_effort maps low/medium/high to ~1,024 / 8,192 / 24,576 tokens. Thinking budgets on Gemini give finer runtime traces and optional summaries for accuracy‑critical work.

Search and distribution implications

• Meta: expect tighter creative cycles for paid placements and owned inventory.
• Search engines: lean into entity depth, answerable questions and first‑party data to protect organic traffic.
• Google search ranking: plan for short‑term volatility; protect evergreen pages with structured snippets and monitoring.

“Media licensing debates — including reporting in the new york times — are reshaping content sourcing and risk.”

Service impact for Australian organisations: strategy, delivery, and risk control

This moment asks us to align practical pilots with governance so delivery teams keep momentum without raising compliance issues.

Natural language and code use cases across marketing, product, and ops

We map the highest‑value use cases by function so teams can start small and show results fast.

• Marketing: rapid copy variants and ad A/B tests with review gates.
• Product: Q&A for help centres, changelogs and spec drafts paired with approval flows.
• Ops: support macros, runbooks and automated reports that reduce manual time.
• Developers: code generation for routine tasks, gated by tests and peer review.

Governance, model control and data boundaries in regulated fields

Enterprise collaborations and publisher licences — for example partnerships with Arizona State University and content deals with News Corp, Vox, Axios and Reddit — show how data partnerships affect governance.

We recommend firm model control policies: prompt logging, approvals, and regular red‑teaming tailored to each regulated field.

Define data boundaries up front. Treat PII, opt‑outs and anonymisation as non‑negotiable. Align vendor terms with Australian privacy law.

“Start with low‑risk tasks, measure lift, then scale with defined roll‑back plans.”

We catalogue common problems: hallucinations, over‑confident answers and prompt drift. Mitigate these with retrieval, tool integrations and human review.

Prioritise delivery: start low‑risk, measure lift, align model choices to SLA and quantify benefits and costs over time so finance and risk teams can approve scale‑up confidently.

Models, model families and choosing the right fit for your use case

Picking a model is about fit: does the tech handle your text, image or vision needs in production?

We start with task classification, then match families to constraints: latency, cost, governance and data residency in Australia.

Language model vs multimodal choices

Compact language models suit fast text generation, chat and low-latency tasks. They cut cost and simplify audits.

Multimodal models handle images, audio and vision tasks. Gemini is natively multimodal and supports structured outputs, embeddings and function calling through an OpenAI‑compatible endpoint.

• Choose reasoning‑optimised models when task complexity needs higher accuracy, despite higher cost and time per call.
• Wrap models behind a stable interface so you can switch providers with minimal rework.
• Embed human‑in‑the‑loop sign‑offs for sensitive outputs and plan lifecycle windows for versioning and deprecation.

Decision: classify task, pick candidate families, benchmark in pilots, then scale with clear logs and residency controls.

API integration choices: OpenAI API vs Gemini API with OpenAI libraries

Deciding how to connect models to your stack determines how fast you can ship and how easy it is to swap vendors later.

Two practical paths: call the native openai api or point the OpenAI client at Gemini by changing api_key to a Gemini key, base_url to the Gemini OpenAI‑compatible endpoint, and selecting a gemini‑2.x model.

Requests, tools and function calling for structured outputs

Structure every request with clear schemas for tools and function calling. Return JSON objects with strict keys so downstream services ingest clean, typed text and data.

Streaming, embeddings and image generation in production

Use streaming for responsive UX. Buffer partial tokens and show progressive UI states, but persist only validated outputs.

Build embeddings pipelines for search and recommendations. Refresh indexes on schedule and version vectors alongside source data.

Migrating or running hybrid stacks

Start with non‑critical endpoints, route by feature flag, measure cost and latency, then expand. Common problems: schema mismatches, token limits and differing safety settings. Abstract these in one interface and keep configuration driven.

“Treat migrations as measurable experiments: route, compare, then commit.”

Code tip: one interface, clear observability, redact sensitive data, attach metadata and capture reasoning settings for audits.

Reasoning and “thinking budget”: balancing cost, speed and accuracy

Balancing token budgets is the practical lever that controls cost, latency and answer quality for production tasks.

Low, medium, high and exact budgets explained

reasoning_effort exposes fixed levels: low (1,024 tokens), medium (8,192) and high (24,576). These simplify choices and predict cost and time at scale.

thinking_budget gives exact control and can include thought summaries for auditing and debugging. Note: the two controls cannot run together on the same endpoint.

• Tune for task mix: use low for short chats, medium for synthesis, high for deep research.
• When to pay more: higher budgets improve accuracy and performance but add latency and cost.
• Risks: over‑thinking yields diminishing returns and hurts conversational UX.

Example evaluation plan: run A/B tests across three budgets, measure accuracy, latency and token cost, then pick defaults by SLA.

We recommend sensible defaults, monitoring to detect drift, and clear documentation so stakeholders understand trade‑offs and spend controls.

Training data, legal strategy and compliance signals

Publisher agreements and court cases have made training data a board‑level issue for many organisations.

We note that OpenAI faced multiple lawsuits in 2023–2024 alleging copyright infringement, while it also struck licensing deals with News Corp, Reddit, Vox and Axios in 2024–2025.

Copyright suits, publishers’ licensing, and your enterprise risk posture

Assess dataset provenance before deployment. Treat licensed sources and scraped content differently in contracts and retention policies.

Legal strategy must include vendor reviews, clear usage limits and clauses for takedowns or indemnities aligned to Australian law.

Safety settings, control problems and audit trails for AGI‑adjacent features

Safety departures in 2024 and governance guidance from May 2023 show the field is evolving fast.

• Enforce safety settings and red‑team protocols for high‑risk flows.
• Log prompts, tool calls and outputs to build auditable trails.
• Map responsibilities across legal, security and product for rapid response.

“Design playbooks for takedowns, disputes and regulator queries to protect service continuity.”

We recommend modelling choices, dataset provenance checks and documentation so your compliance outcomes support innovation, not block it.

Performance measurement: evaluating outputs and operational SLAs

Good measurement turns model outputs into reliable business signals.

We set KPIs that map latency, cost and quality to stakeholder SLAs. This keeps product teams aligned and procurement focused on real spend.

Latency, cost per token and quality benchmarks

What to measure: end‑to‑end time, tokens per call and an accuracy score against domain test sets.

• Design test sets that reflect real questions and edge cases for your users.
• Use structured output parsers and function calling to validate outputs before they reach customers.
• Integrate streaming in the UI to improve perceived speed while logging true completion time.

We benchmark competing models with consistent prompts and an acceptance criteria list. Then we run A/B comparisons using a code harness to compare providers and versions safely.

“Measure latency, token cost and quality together — one metric alone hides trade‑offs.”

Consider training data and retrieval strategy before you decide to train models. Often prompt tooling and better retrieval give more lift for less cost.

We maintain a running list of metrics and dashboards so teams spot regressions early and scale with confidence.

Content operations under change: adapting to feature rollouts across platforms

Platform updates force a rethink of how we plan, produce and approve content.

We redesign editorial workflows to pair editors with model-trained assistants for ideation and drafting. Sora (for Plus/Pro) and DALL·E remain useful for image generation, while Gemini adds vision and audio understanding on paid tiers.

Editorial workflows for text, image and video with assistants

We orchestrate text and image production with clear legal and brand checkpoints. Standard prompts, templates and a shared glossary keep voice consistent and cut revision loops.

We integrate vision for asset tagging and content QA so teams scale without extra review time. We train many people across teams to avoid bottlenecks and ensure safe deployment.

Example rollout: pilot one business line, measure lift, then expand to adjacent teams with CMS and DAM integrations.

• Manage rights for generated media and align licenses to your risk appetite.
• Connect outputs to analytics so performance feeds planning.
• Build fallbacks for outages to keep publishing on schedule and meet accessibility and Australian English guidelines.

Search engine realities: OpenAI SearchGPT, Google Search and your traffic mix

Answer experiences now capture attention at the search surface, reshaping downstream traffic and conversions.

SearchGPT appears in OpenAI’s product list and Gemini‑style models support structured outputs, embeddings and image understanding via compatible endpoints. These capabilities can drive answer experiences and entity retrieval that sit above traditional listings.

What this means for your site:

• Answer engines and zero‑click patterns shift clicks to summaries and entity cards.
• Protect core revenue by throttling experiments while you learn which snippets convert.
• Build entity‑first content so models surface your brand accurately in summaries.

Capture the user questions that lead to high intent journeys. Design outputs that are both user‑friendly and machine‑readable: clear text, schema, and concise structured data. Monitor google search volatility over time and test models for branded and generic queries to spot gaps early.

“Invest in text and structured data aligned to evolving snippets; report traffic swings clearly to stakeholders.”

Enterprise implementation playbook for Australia

We map a stepwise rollout that protects data, meets Australian residency rules and builds skills across teams. This playbook balances speed with safe controls so projects deliver value without widening risk.

Security, privacy and data residency considerations

We set policies for security, privacy and data residency tailored to Australian regulation. Define access control, key management and segregation of duties up front.

Practical controls:

• Local data zones and encryption at rest with documented export rules.
• Access control lists, role separation and key rotation policies.
• Vendor due diligence and model selection standards tied to your field.

Change management and skills uplift for many people

We design staged change programs to train many people while keeping delivery momentum.

• Deployment waves to limit blast radius and prove value quickly.
• Experiment tracking, approvals and audit logs for each wave.
• Escalation paths for incidents and content disputes with clear accountability.

“Align legal strategy with platform terms and publisher agreements and keep governance transparent to leadership.”

We also factor in that OpenAI defines general intelligence as systems that outperform humans at the most valuable work and that Microsoft Azure remains a major compute partner. Governance recommendations from May 2023 and publisher licensing shifts (2024–2025) inform our legal strategy and controls.

Sector use cases: media, government, finance, healthcare and industry

Sector demands shape where models deliver the most measurable value today.

Use cases and examples grounded in natural language and structured data

We outline practical use cases with clear ROI so teams can prioritise pilots fast.

• Media: content packaging, automated summaries and syndication checks that respect licensing shifts such as deals with News Corp, Vox and Axios.
• Government: service FAQs, multilingual answers and accessibility‑first flows that reduce calls and speed outcomes.
• Finance: product explanations, compliance disclosures and question routing with auditable trails.
• Healthcare & Industry: triage assistants, document Q&A and safety checklists with human sign‑offs where risk is high.

We match model selection to task complexity and compliance. Use compact models for high‑volume chat, large language families for summarisation and o‑series or reasoning models for deep synthesis.

“Design natural language interfaces that guide questions and surface sources; keep humans in the loop for critical decisions.”

We define feedback loops per field, script question flows, and recommend guardrails and monitoring to maintain quality at scale.

Ecosystem and runway: funding, chips and infrastructure partnerships

We outline the funding and partnerships that shape capacity and pricing for future model rollouts. This matters for procurement, timeframes and service continuity in Australia.

Microsoft Azure, Apple integrations and CoreWeave access

Microsoft’s US$13 billion investment and Azure compute remain central to scale and pricing for large workloads.

In June 2024 a partnership with Apple broadened distribution on iPhones, changing privacy and on‑device expectations for consumers.

In March 2025 the organisation signed an US$11.9 billion agreement with CoreWeave, gaining access to 250,000+ NVIDIA GPUs and minority shares—this lifts short‑term capacity for training and inference.

Custom chips, Nvidia constraints and future rollouts

Broadcom is designing custom silicon to be manufactured at TSMC 3 nm, targeting mass production in 2026 to diversify beyond NVIDIA GPUs.

That chip strategy aims to reduce supply risk and support heavier throughput when you train models or serve high‑volume inference.

“Plan procurement around these milestones and prioritise workloads if supply slips.”

• Capacity planning: size compute for peak training windows and steady serving SLAs.
• Data pipelines: ensure throughput and storage match training cadence and evaluation needs.
• Sustainability: public debate compares energy use to nuclear power; factor this into vendor choice.

Actionable steps: align procurement windows to these milestones, stage training runs, and set contingency plans to avoid costly pauses. We link infrastructure realities to deployment timelines so you can budget and move with confidence.

Get help with your rollout and meta changes

When platform changes threaten performance, a short technical audit yields rapid, testable fixes. We run focused checks so you can decide quickly and act with confidence.

Rapid assessment: models also to consider, from o1 to Gemini 2.5

We shortlist models also suitable for your goals, comparing o1, GPT‑4‑class and Gemini 2.5 for cost, latency and reasoning needs.

Implementation support: APIs, governance and content strategy

We implement the right api path for you — native openai api or using openai libraries pointed at Gemini (change api_key, base_url, model). This keeps interfaces stable and reduces rework.

• Harden governance: prompt policies, logs and approvals.
• Fix integration problems fast: schema mismatches, rate limits and safety settings.
• Provide code reviews and pairing to get engineers productive.
• Stabilise content strategy so performance holds during transitions.
• Set dashboards for cost, latency and quality with clear pilot criteria.

“Operator and Deep Research agents are live for Pro users with early access limits; factor those caps into pilot design.”

We answer your questions in plain language and translate them into concrete work items. Contact hello@defyn.com.au if you are struggling with your meta rollout — we’ll help you ship safely and on time.

Conclusion

This wrap-up highlights the choices that matter and the steps to test them.

We give clear details and a short list of actions so teams can move from pilot to scale. Start small: pick one feature, test outputs, measure performance and log results.

Example steps: run a short pilot with o1 or a compact language model, validate training data and observability, then refine requests to avoid common problems. Consider time‑sensitive factors such as funding, chips and integrations when you plan roadmaps.

We can help you evaluate models, train models and integrate via the openai api or compatible endpoints. If you need support with a meta rollout or urgent requests, contact hello@defyn.com.au — we’ll align stakeholders and help you ship with confidence.