What Small and Mid-Sized Companies Should Do Before Long-Horizon Agents Arrive

The most important change in AI is not that a model can answer another benchmark question. It is that models are beginning to behave like persistent operators. They can read a codebase, use tools, write a patch, run tests, recover from mistakes, keep context, and continue. In other words, the industry is moving from “AI as an assistant” toward “AI as an execution layer.”

For a company with 60 or 100 employees, this is not an abstract research topic. If long-horizon agents become reliable over the next one to two years, they will change how work is organized, how software is priced, and what customers expect from vendors. The right question is no longer whether a company should “use AI.” The question is whether the company’s processes, data, products, and business model are ready for autonomous execution.

1. The key metric is no longer intelligence in one step. It is duration.

METR’s 2025 work on long-horizon tasks offers a useful way to frame the shift: measure AI systems by the length of tasks they can complete independently, where task length is defined by how long a human professional would need. METR reported that the task length frontier, at roughly 50% reliability, has been doubling about every seven months over the past six years. The same work also makes an important caution clear: today’s systems can be very strong on short tasks, but still fail on tasks that require experts to work for hours.

This is the right lens for business. Most valuable work is not a single answer. It is a chain: understand the situation, inspect the system, choose a plan, execute, hit an exception, recover, verify, and report. Once AI systems become dependable across that chain, they stop being merely productivity tools. They become a new production layer.

2. The leading companies are already building toward this

The public signals are consistent.

• Coding agents are becoming a central product category. Anthropic’s Claude Sonnet 4.5 release described the model as strong in coding, complex agents, and computer use. Anthropic also said it had observed the model maintaining focus for more than 30 hours on complex multi-step tasks. Whether one treats that as a product claim or a technical milestone, the direction is clear: long-horizon execution is now a primary frontier, not a side feature.
• Agent infrastructure is becoming productized. Anthropic’s release also discussed its Agent SDK and the practical problems behind long-running agents: memory, permission systems, context management, subagents, and user control. These details matter. A model is only the engine. Enterprises need the brakes, steering, dashboard, logs, and safety mechanisms.
• Security has already produced real-world examples. Google Project Zero and Google DeepMind’s Big Sleep project publicly described an AI agent finding a previously unknown exploitable memory-safety issue in SQLite before it reached an official release. That is not the same as saying AI can replace expert security researchers today. It does show that AI-assisted vulnerability research has moved from toy examples into real software.
• Self-improving engineering loops are becoming concrete. Google DeepMind’s AlphaEvolve combines Gemini models with automated evaluators and an evolutionary framework. DeepMind reported uses in data center scheduling, TPU-related circuit design, and Gemini training efficiency. The deeper lesson is not one specific percentage improvement. It is the pattern: let AI generate code, run it, score it, select the best variants, and iterate in an environment where progress can be measured.
• Enterprise adoption has crossed the experimentation threshold. Stanford HAI’s 2025 AI Index reported that 78% of organizations used AI in 2024, up from 55% in 2023, while global private investment in generative AI reached $33.9 billion. The same report also notes rising AI incidents and governance pressure. In plain terms: companies are using AI more, spending more, and taking on more responsibility.

Put together, these signals point in one direction. Model companies are extending execution length. Researchers are building better measurement frameworks. Security teams are testing agents on real code. Enterprises are moving from pilots to workflow redesign. A small or mid-sized company that still treats AI as a writing helper or a code autocomplete tool is already behind the organizational curve.

3. The position of 60- to 100-person companies: exposed, but not doomed

The risk is straightforward. Many small and mid-sized software companies survive on human-heavy delivery: a client asks for a system, product managers clarify it, designers draw it, developers build it, testers check it, and the company maintains it. This model worked because software production had enough friction.

Long-horizon agents will first compress the low-barrier parts of that work: ordinary admin panels, simple mobile apps, dashboards, CRUD systems, basic automation scripts, routine testing, marketing copy, and support knowledge bases. These services will not vanish overnight, but their price and delivery time will collapse. A client who used to accept a three-month delivery cycle will begin asking why the first version cannot appear in three days and go live in two weeks.

The opportunity is that smaller companies can change faster than large organizations. A 60-person company can rebuild its internal operating system in months if leadership is serious. It can become agent-native before a larger incumbent finishes its steering committee meetings.

4. Will app development companies run out of work?

Many ordinary apps will become cheap. Template apps, basic websites, simple internal tools, small CRUD systems, and low-complexity mobile apps will be heavily commoditized.

But that does not mean all app companies disappear. The value will move away from drawing screens and toward five harder capabilities:

• Deep business understanding. Healthcare, finance, logistics, education, manufacturing, and cross-border commerce are not solved by a prompt. The company that understands edge cases, regulation, legacy processes, incentives, and messy data will still matter.
• Integration and accountability. Enterprise software is not only an interface. It involves identity, permissions, audit trails, payments, contracts, inventory, finance, compliance, and incident response. AI can generate code; customers still need someone responsible for architecture, deployment, reliability, and risk.
• Private workflow data. The moat of an AI-native product is not the UI. It is the workflow and data loop. The closer a vendor is to real work, the better it can place agents into the right environment.
• Distribution and trust. When software becomes easier to generate, customers need trusted partners to choose, verify, maintain, and take responsibility for systems. Trust becomes more valuable, not less.
• Agent environments. Some of the best opportunities will not be “build an app.” They will be “turn this industry workflow into a safe environment for agents”: APIs, permissions, sandboxes, evaluations, logs, rollback, billing, and human escalation.

So the transformation path for an app development company is not simply “build apps faster.” It is to become an AI business transformation partner: understand the customer’s work, build agent-ready environments, connect outputs to real systems, and keep improving the workflow after launch.

5. What companies should start doing internally now

Build a managed AI workbench

Employees should not be using random tools with random accounts and random customer data. A company needs a unified model entry point, permission levels, cost tracking, logging, data policies, and a clear rule for what can and cannot be uploaded. In the long-horizon era, AI usage is part of the production system. It has to be managed like one.

Turn work into task packages

Agents are bad at vague wishes and better at structured missions. A company should define repeatable task packages: input, tools, constraints, acceptance criteria, rollback plan, and human approval points. Future automation will depend less on whether a model is “smart” in the abstract and more on whether the company’s work can be described, executed, and checked.

Create agent SOPs for every department

• Engineering: requirement understanding, code search, implementation, tests, review, release notes, rollback.
• Product: competitor research, interview synthesis, PRD drafts, experiment design, metric reviews.
• Sales: customer background research, proposal drafts, tender documents, CRM updates, follow-up reminders.
• Support: ticket classification, knowledge-base updates, standard replies, escalation rules.
• Operations: content production, channel campaigns, data analysis, A/B testing.
• Finance and legal: contract pre-review, invoice matching, payment checks, risk clause marking, always with human approval.

Build internal evaluations, not just model opinions

Every company should create a private evaluation set from its own work: 50 historical requirements, 50 bugs, 20 client proposals, 20 complex support tickets, and 20 operational analyses. When the company changes models or tools, it should rerun the same tasks and measure whether the outputs are usable, safe, and deliverable. A leaderboard score is not a business result.

Start building company memory

Long-horizon execution needs memory. This does not mean dumping chat logs into a vector database. It means structured operational knowledge: customer context, architecture decisions, failure patterns, release history, coding standards, sales language, contract risks, and metric definitions. The earlier this is cleaned and classified, the more useful future agents become.

Train people to become supervisors and designers of work

Many roles will not disappear immediately, but their center of gravity will move. Developers must learn to specify tasks, review patches, design tests, and manage context. Product managers must design processes that agents can execute. Sales teams must turn market knowledge into reusable assets. Executives must learn to evaluate AI-generated work by risk and evidence, not just speed.

Create a small agent transformation team

A 60- to 100-person company does not need a large AI lab. It does need a serious core team of three to five people: a business owner, an engineering lead, a process or operations lead, and a security or compliance owner, with data engineering support when needed. This team should not build demos. It should convert the company’s ten highest-frequency workflows into agent-executable systems.

6. A practical 12-month roadmap

First 30 days: map the work

• List all repeated work and rank it by time spent, frequency, and risk.
• Set minimum data rules before employees upload sensitive client information.
• Choose three low-risk pilots: daily summaries, support ticket classification, code review assistance, or proposal drafting.
• Create a shared library of prompts, SOPs, examples, and failures.

30 to 90 days: make repeatable workflows

• Turn pilot processes into task packages with inputs, tools, acceptance criteria, and approval points.
• Build an AI-assisted engineering pipeline from requirement to pull request, tests, review, and release notes.
• Create an internal evaluation set and rerun it every two weeks.
• Start a company knowledge base, but only with cleaned and permissioned data.

Three to six months: put AI into core delivery

• Pick one customer delivery process and redesign the whole chain from research to deployment.
• Move pricing away from pure headcount and toward outcomes, ongoing service, or efficiency gains.
• Expose product APIs, logs, permissions, and rollback mechanisms so agents can operate safely.
• Create an audit trail: who asked the agent to do what, what data it used, and what systems it changed.

Six to twelve months: rebuild the business model

• Stop selling ordinary development labor as the main value proposition.
• Package the company’s strongest industry knowledge into agent environments: data connectors, evaluation criteria, process templates, and compliance modules.
• Restructure delivery teams around a smaller number of experts, multiple agents, and strong evaluation gates.
• Upgrade customer success from bug fixing to continuous improvement of business outcomes.

7. Three management mistakes to avoid

• Treating AI only as a cost-cutting tool. Cutting cost may help for a quarter. It will not build organizational capability. The better goal is to turn the same team into a higher-leverage team.
• Buying tools without changing workflows. Long-horizon agents are not browser extensions. Without clear tasks, permissions, data boundaries, and acceptance criteria, they will produce more half-finished work.
• Building strategy on rumors. Claims about self-training models or massive compute clusters may be directionally interesting, but unless they are public and verifiable they should remain scenarios, not facts. Strategy should be based on measurable capabilities and business tests.

8. The bottom line

The long-horizon era will not arrive in a single dramatic moment. It will arrive gradually: first agents that can reliably handle half-day tasks, then one-day tasks, then week-long work. Each increase in duration will remove another layer of human process from the old operating model.

The best defense for a small or mid-sized company is to become an early operator of this new capability. Structure the work. Govern the data. Build evaluations. Train employees to manage agents. Turn products from interfaces into executable environments.

Software companies can no longer define themselves as organizations that merely produce software. They must become organizations that produce verified automation. They can no longer sell only human time. They must sell outcomes. They can no longer think only in apps. They must help customers rebuild work itself.

The companies that make this shift early will still have a seat at the table. The companies that remain trapped in traditional project delivery will feel the price collapse first.

References

• METR, “Measuring AI Ability to Complete Long Tasks”, 2025-03-19.
• Anthropic, “Claude Sonnet 4.5”, 2025.
• Google Project Zero, “From Naptime to Big Sleep: Using Large Language Models To Catch Vulnerabilities In Real-World Code”, 2024-10.
• Google DeepMind, “AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms”, 2025.
• Stanford HAI, “2025 AI Index Report”, 2025.