Tech Trends for 2026 - Agentic AI, MCP, and What's Actually Happening

18th October 2025

The Real Story Behind the Hype

Every year we get breathless predictions about technology. Most are wrong. Some are marketing.

But 2025 has been... different. Python overtook JavaScript on GitHub for the first time in a decade. AI code generation went from "interesting" to "84% of developers use it." The Model Context Protocol emerged as an actual standard.

So what's actually happening in 2026? Let me separate signal from noise.

1. Agentic AI: Gartner's #1 Strategic Trend

What it actually is: AI systems that make decisions, plan multi-step workflows, and take actions autonomously. Not just chatbots that respond to prompts.

Why it matters: Gartner says by 2028, 33% of enterprise software will include agentic AI, and 15% of work decisions will be made autonomously.

Real-World Examples (Not Vaporware)

New employee onboarding: Agents automatically provision access to systems, order equipment, schedule training. No human in the loop unless something fails.

Domain-specific collaboration: Multi-agent systems where specialized agents work together. One handles cloud infrastructure, another manages security, another optimizes costs.

Enterprise deployments: Block (Jack Dorsey's company) and Apollo are integrating agentic systems into production. Early adopters are seeing 10-25% EBITDA gains.

The Reality Check

Stack Overflow's 2025 survey tells a different story:

84% use or plan to use AI tools (up from 76%)
BUT positive sentiment DECREASED from 70%+ to 60%
46% don't trust AI accuracy (up from 31%)
66% cite "AI solutions that are almost right, but not quite"
45% say debugging AI-generated code is more time-consuming

My take: Agentic AI will be huge for bounded, repetitive tasks. But the hype is ahead of reality. Trust is declining even as usage increases.

2. Model Context Protocol (MCP): The USB-C for AI

This is the most important development most people aren't talking about.

The Problem

Before MCP, connecting an AI to your data sources meant custom implementations for everything:

Want Claude to read Google Drive? Custom integration.
Want it to access Slack? Another custom integration.
Want it to query Postgres? Yet another integration.

Every AI × every data source = N×M integration nightmare.

The Solution

MCP is an open standard for connecting AI assistants to data sources and tools. One protocol, infinite connections.

Think of it like USB-C replacing 47 different charging cables.

Why Anthropic (Claude) Built This

Anthropic announced MCP in November 2024. OpenAI and Google DeepMind both adopted it. That's huge.

Pre-built MCP servers exist for:

Google Drive, Slack, GitHub
PostgreSQL, MySQL, SQLite
Stripe, AWS, Docker
Puppeteer, Filesystem access

How it works:

// MCP client connecting to servers
const mcpClient = new MCPClient();

// Connect to multiple data sources via MCP
await mcpClient.connect('mcp://google-drive');
await mcpClient.connect('mcp://postgres');
await mcpClient.connect('mcp://slack');

// AI now has access to all three through standard protocol

Developers write either:

MCP servers (expose data sources)
MCP clients (AI applications that consume data)

Not both. That's the power.

Real Adoption

Block: "Open technologies like MCP are bridges that connect AI to real-world applications"
Dev tools: Zed, Replit, Codeium, Sourcegraph adding support
Apollo: Integrated into production

The catch: Security issues exist (prompt injection, excessive permissions). Safari doesn't fully support it. OpenAI has a competing proprietary approach.

But the industry is consolidating around MCP as the standard. InfoQ's 2025 DevOps report literally says: "Everyone's adopting MCP."

3. Python Overtook JavaScript (Because of AI)

This is wild. Python is now the #1 language on GitHub.

JavaScript held that spot for over 10 years. Python took it in 2024.

Why?

AI/ML explosion (92% increase in Jupyter Notebook usage)
Data science growth
Ease of use for non-traditional programmers

GitHub stats:

7 percentage point increase for Python (2024-2025)
59% surge in contributions to generative AI projects
98% increase in number of generative AI projects
70,000+ new public AI projects in 2024

This isn't temporary. AI is driving massive Python adoption.

4. Platform Engineering: The Evolution Beyond DevOps

Gartner's prediction: By 2026, 80% of software engineering organizations will establish platform teams.

What Platform Engineering Actually Is

It's treating internal developer platforms as products:

Self-service tools that minimize friction
Reusable services, not tickets to ops teams
Measurable outcomes, not just infrastructure

Examples:

Internal developer portals with one-click environment creation
Standardized deployment pipelines that "just work"
Observability baked in, not bolted on

Why Now

The stats:

54% of IT departments expect increased spending in 2026
24% expect spending increases >10%
Teams using automation perform 27% better in open source
10.54 billion GitHub Actions minutes in 2024 (up 30% from 2023)

Organizations are realizing: giving developers better tools is cheaper than hiring more developers.

AIOps: AI for Operations

86% of IT leaders say rapid software releases are important. But humans can't keep up.

AI is filling the gap:

AI-powered CI/CD pipelines that self-optimize
Predictive analytics for incident management
Auto-scaling with AI-driven monitoring
Anomaly detection in logs and metrics

The AIOps market is growing ~15% compound annually. It's not hype - it's being deployed.

5. Edge Computing: 75% of Data Processed at Edge by 2025

Gartner stat: 75% of enterprise data will be processed at the edge (up from 10% in 2018).

Why it matters:

AI at the edge (NVIDIA Jetson bringing inference to devices)
5G + edge = <5ms latency (vs 20-40ms for cloud)
74% of global data processed outside traditional data centers by early 2030s

Real Use Cases

AI-driven retail: Kiosks with local AI inference, instant recommendations Predictive maintenance: Manufacturing sensors processing data locally Autonomous vehicles: Requiring 4000+ TOPS, can't wait for cloud round-trip Smart healthcare: Remote diagnostics with instant results

The challenges:

Device management at scale (75 billion connected devices in 2025)
10-15% of edge locations have connectivity issues
Security concerns (larger attack surface)

The trend: Hybrid models blending edge and cloud. Not "edge vs cloud" - it's "edge + cloud."

6. WebAssembly: From Niche to Mainstream

Current adoption: 4.5% of websites use WASM (up ~1% annually)

That sounds small, but look who's using it:

Figma: Entire design tool in browser
AutoCAD Web: Complex CAD operations
Zoom & Google Meet: Video conferencing
Visa: Payment processing
American Express: Potentially largest commercial WASM deployment (internal FaaS platform)
Unity WebGL: AAA gaming in browser

WebAssembly 3.0 (October 2025)

Just released with massive improvements:

Garbage Collection: Makes high-level languages viable
64-bit Memory: Enables previously impossible applications
Component Model: Universal runtime for browsers, edge, cloud
20× faster than JavaScript in compute-intensive tasks

Real Performance Gains

3-5× performance in DOM-free operations vs JavaScript
30-50% reduction in AI inference processing time
Faster startup than Docker containers (ideal for serverless)

The future: Python integration is top 2025 priority. Goal: every Python developer can write WASM apps.

7. Cybersecurity: 1.5 Million Unfilled Jobs by 2025

India alone: 1.5 million unfilled cybersecurity positions by 2025.

This is a massive opportunity:

Average salary in India: ₹3-40 LPA ($3,600-$48,000)
Average salary in US: $65,000-$157,000
Job growth: Faster than average through 2033

What's Changing

DevSecOps becoming standard:

Over 50% of DevOps teams now handle security
Security automation, compliance-as-code
"Shift-left" security in CI/CD

Real results:

94% of top 50 open source projects use OpenSSF Scorecard
39 million secret leaks detected via secret scanning (2024)

If you're looking for a career path: cybersecurity + AI = extremely high demand.

8. AI Job Market Reality Check

The Positive:

75%+ of venture capital investors will use AI by 2025
AI expected to create 2.4 million jobs
9% of all new tech jobs by 2025

The Negative:

Less than 5% of jobs can be fully automated
60% can be partially automated
256 billion lines of code generated in 2024 → 600 billion projected in 2025

The prediction: 90% of code will be AI-generated by 2026.

What this means: You still need developers. But developers who can work effectively with AI tools will be 10× more productive than those who can't.

Salary Ranges (2025 Actual Data)

India

PHP/Laravel Developer: ₹3.6-19.2 LPA ($4,300-$23,000)
Next.js/React Developer: ₹3.6-30 LPA ($4,300-$36,000)
AI/ML Engineer: ₹5-25 LPA ($6,000-$30,000)

United States

Full-Stack Developer: $56,000-$165,000 (average: $102,000)
Laravel Developer: $83,000-$165,000 (average: $117,000)
Next.js Developer: $120,000-$232,000 (average: $128,000)
AI/ML Engineer: $100,000-$200,000+ (high demand)

Europe

PHP/Laravel: €30,000-€90,000 ($32,400-$97,200)
Next.js/React: €35,000-€133,000 ($37,800-$143,640)

Remote work from lower cost-of-living areas is creating arbitrage opportunities.

What You Should Actually Do in 2026

Immediate Actions:

Experiment with AI tools (cursor, GitHub Copilot, Claude) for coding
Learn about MCP if you're building AI applications
Pick up Python if you haven't already (it's now essential)
Start using platform engineering practices in your team
Consider edge computing for latency-sensitive applications

Strategic Priorities:

Don't ignore AI - 84% of developers are using it
But don't trust AI blindly - 46% don't trust accuracy
Focus on fundamentals - good developers who use AI >> AI alone
Build for edge + cloud, not one or the other
Security is a feature, not an afterthought

Career Advice:

High demand: JavaScript/TypeScript, React, Next.js, Python, AI/ML
Best combo: Full-stack + AI + cloud/DevOps
Safest bet: Learn fundamentals deeply, use AI as a tool

The Bottom Line

2026 isn't about one killer technology. It's about:

Agentic AI handling routine decisions
MCP standardizing AI-data connections
Platform engineering making developers more productive
Edge computing processing data where it's created
AI-augmented development becoming standard

The hype is real, but so are the challenges. Trust in AI is declining even as usage explodes. That tension will define 2026.

Stay technical. Build things. Adapt quickly. The tools have never been better.

Resources

The future is being built right now. Be part of it. 🚀