AI Agents and the IT Market: What Will Change by 2027
The IT services market in 2024–2025 is undergoing its most significant shift in 20 years. AI agents — autonomous systems capable of executing multi-step tasks without continuous human involvement — are beginning to replace entire functions that previously belonged exclusively to IT professionals. According to McKinsey (2024), up to 30% of current IT tasks could be performed without human involvement by 2030. But this doesn't mean the IT market will shrink — it's transforming, creating new categories and roles faster than it's eliminating old ones.
What Is an AI Agent and How It Differs from Regular AI
An AI agent isn't just a model that answers questions. It's a system that autonomously plans, selects tools, and executes a chain of actions to achieve a goal. The difference is fundamental.
A standard language model (ChatGPT, Gemini) answers one prompt and stops. An agent receives a task like "deploy an API with authentication," writes the code itself, runs tests, fixes errors, deploys to the server, and reports the result — without intermediate human involvement.
Examples of agents already working in production as of 2025: Devin (autonomous developer by Cognition), GitHub Copilot Workspace (full cycle from issue to pull request), Cursor Agent (refactoring entire codebases). These aren't experiments — they're tools companies pay real money for.
Which IT Market Segments Are Changing Most
Not all parts of the market will change equally. Some segments face direct pressure, while others gain new growth momentum.
| IT Segment | AI Agent Impact | Timeline |
|---|---|---|
| Outsource development (standard tasks) | High pressure — ↓ demand for juniors | 2025–2026 |
| QA and testing | Partial automation of regression testing | 2025 |
| IT support (L1/L2) | Strong pressure — agents handle up to 70% of tickets | Already now |
| Cloud infrastructure | Growth — agents need compute resources | 2024–2027 |
| Cybersecurity | Growth — protection from agents and agent-assisted attacks | 2025–2027 |
| AI infrastructure (MLOps, vector DBs) | Explosive growth | Already now |
Most telling — IT support. Major companies (Klarna, Salesforce) have already reported reducing L1 support costs by 40–70% after deploying agent systems. This isn't a forecast — these are 2024 reports.
Software Development: What's Automated, What Stays Human
The fear that agents will replace all programmers is exaggerated. The fear that they won't change the profession at all is naive. Reality is more nuanced.
What agents already do well:
- Writing boilerplate code (CRUD operations, API wrappers, config files)
- Refactoring by explicit rules
- Writing unit tests for existing code
- Fixing bugs from logs and tracebacks
- Documenting existing code
What still requires humans:
- Understanding business context and negotiating with stakeholders
- Architectural decisions for non-standard requirements
- Debugging complex race conditions and distributed systems
- Making trade-offs between performance, cost, and complexity
Junior tasks get automated, senior roles get amplified. A developer who can work with agents as tools replaces a team of 3–4 juniors. Demand for such specialists is growing, and salaries follow.
IT Services and Outsourcing: How Pricing Is Changing
For outsourcing companies, AI agents are simultaneously a threat and a competitive advantage tool. The standard model of "selling human-hours" breaks down when an agent does in 2 hours what a person did in 40.
Three scenarios already playing out in the market:
Scenario 1 — Commoditization: companies selling standard development at hourly rates are losing margin. Clients see that an agent does the same thing cheaper and push on price. Average project value dropped 20–35% in 2024 for companies that haven't integrated AI into their processes.
Scenario 2 — Productization: strong outsourcers repackage services into products with predictable outcomes. Instead of "100 hours of development" — "a ready integration module with a guarantee." AI reduces their cost base, margins grow.
Scenario 3 — AI-native companies: startups built natively on an agent stack can compete with 10–15 person teams using 2–3 employees. This changes the competitive landscape for all market players.
New Market Categories That AI Agents Are Creating
While some segments contract, others grow at double speed. Agents create demand for infrastructure that didn't previously exist.
Agent orchestration — tools for managing multiple agents, coordinating them, and maintaining oversight (LangGraph, CrewAI, AutoGen). This is a rapidly growing segment with a shortage of specialists.
Vector databases — agents need long-term memory. The vector DB market (Pinecone, Weaviate, Qdrant) has grown 4x since 2022 and continues expanding.
Agent monitoring and security — a new niche: tools that watch what agents do, prevent unintended actions, maintain audit trails. Few mature solutions exist here yet — an opportunity for developers.
Prompt engineering and AI integrations — companies pay for configuring agents to specific business processes. This is a service that's difficult for agents themselves to automate.
Which Skills Have Sharply Risen in Value in 2024–2025
According to LinkedIn Talent Insights and the Stack Overflow Developer Survey 2024, here's what changed in demand structure:
| Skill | Demand Growth (2023→2025) | Median Salary (US, $) |
|---|---|---|
| LLM/Agent development (LangChain, LlamaIndex) | +340% | $165,000–185,000 |
| MLOps / AI infrastructure | +180% | $155,000–175,000 |
| Cybersecurity (AI attacks & defense) | +90% | $140,000–160,000 |
| Prompt engineering (enterprise) | +210% | $120,000–145,000 |
| Data engineering | +75% | $130,000–155,000 |
| Basic web development (CRUD) | −15% | $85,000–110,000 |
The trend is clear: skills related to building and managing AI systems command increasingly higher value. Skills that agents can already replicate are becoming cheaper.
What to Learn Right Now to Stay Ahead of the Market
Whether you're an IT professional or just entering the field, here's a concrete roadmap for 2025–2026 with real resources.
For developers: add LLM API skills (OpenAI, Anthropic) to your toolkit. Starting point: "Building Systems with the ChatGPT API" on DeepLearning.AI (free, 4 hours). Next step: LangChain or LlamaIndex for building agent pipelines.
For QA engineers: learn AI-assisted testing. Tools like Playwright + AI plugins are transforming the profession, not eliminating it. A specialist who can configure agent-based testing is worth more than one writing test cases manually.
For IT managers and architects: understanding agent architectures is becoming mandatory. Andrew Ng's "AI for Everyone" on Coursera ($49) provides the non-technical understanding needed for implementation decisions.
For those entering IT from scratch: don't pursue junior development in standard stacks without simultaneously learning AI tools. Python + ML basics + LLM API skills opens doors where there's a talent shortage, not a surplus.
The IT market isn't shrinking — it's redistributing. Those who adapt to working with AI agents as co-workers will gain. Those who compete with agents in what agents do better will lose.