Why Agentic AI Will Matter in 2026

By SaratogaSoftware Artificial Intelligence (AI)

By Willo van der Merwe. When it comes to Agentic AI, the last two years have been predominantly about exploration. We’ve tested models, prototyped ideas, broke things, and learned where generative AI genuinely helps, where it doesn’t, and where it simply adds noise.

This year feels different. We’re moving out of the demo phase, out of the hackathon-only phase, out of “let’s see what this thing can do” mindset.

This year we’re seeing agentic AI move into operational reality. In particular, well-bounded domains. As systems that plan, act, use tools, collaborate, escalate, and adapt, they are starting to find their way into real workflows, real processes and real environments. Not as a novelty. As part of how work actually gets done.

See our Agentic AI case study for real-world impact.

For us at Saratoga, we’re less concerned about keeping up with the Jones’s. What matters to us is that we pay attention, form grounded judgement and apply real technical craftsmanship to problems that matter.

We often repeat a line from Ginni Rometty, former CEO of IBM:

“We won’t be replaced by AI, but by people using AI.”

While we eagerly and thoroughly test and use the latest AI tools and tech available, we know that our edge doesn’t come from the tools themselves. It comes from how we adopt them, how we shape them, and how we integrate them responsibly into the organisations we work with.

Skip ahead: Contact Saratoga for expert Agentic AI solutions.

The Real Shift: From AI Models to AI Agents

Agentic AI has moved beyond the idea of “better chatbots” and into a different way of building systems altogether. Instead of simply responding to prompts, agents are able to plan ahead, operate in loops, decide when to call tools, when to retry or hand work off, and when escalation is the right option. They carry context through memory, produce concrete artefacts along the way, and operate within clear guardrails, which makes them far better suited to real operational environments than conversational demos ever were.

Our recent internal work with Roo-Code gave us an early, practical glimpse of that shift. What started as a coding assistant quickly evolved into something closer to an architectural analyst. It helped us make sense of a complex Azure environment in hours instead of days. It didn’t remove the need for experience or judgement. It amplified it.

That moment mattered to us because it wasn’t theatre, it was practical, usable benefit. What we saw internally is now showing up clearly across the industry.

Explore: AI Adoption: Myths, Realities, and What It Really Takes to Get It Right

Signals from Industry: What 2026 Is Shaping Up to Be

We paid close attention to what emerged from AWS re:Invent in 2025. Not announcements, but patterns and a few stood out.

A Flood of Agent Frameworks, and a Need for Discernment

There is now a growing ecosystem of agent frameworks across languages and platforms. Amazon announced updates to their Python SDK and introduced a TypeScript-based Strands Agents SDK. Vercel showcased their own deeply developer-centric approach. Others continue to release new abstractions at pace.

All useful. But see, the important signal was not the tools themselves, rather it was the convergence underneath them.

Many AI agent systems are converging around similar calling patterns

Tools → LLM call → evaluate → loop.

For us, that matters. It means the long-term advantage won’t come from picking “the right” framework this quarter. It will come from understanding the architectural patterns behind them, and designing systems that can evolve as tooling inevitably changes. Our role becomes less about chasing libraries, and more about shaping operating models that make sense inside each client’s environment.

Interesting read: AI Agents: What We’ve Learned So Far

Coding Agents Are Where Reality Is Moving Fastest

If there is one agent category to watch closely, it is coding agents. Code has structure. It compiles or it doesn’t. Tests pass or they fail. Outputs can be validated. That makes reinforcement and feedback loops far more stable than in many other domains.

Across re:Invent, many developers were already working alongside tools like Claude Code, Cursor and similar systems. Amazon’s Kiro approach to spec-driven development is gaining interest. The pace is quick.

This aligns directly with what we experienced through Roo-Code.

Coding agents already deliver meaningful productivity gains. And they are improving fast. This is where developers and technical consultants will likely feel the impact first – not as replacement, but as an extension of how serious engineering work gets done.

The Emergence of “Agent Fabric” as a New Runtime Layer

One of the more interesting threads coming out of re:Invent was the idea of an “agent fabric”. A runtime layer designed not for HTTP requests, but for:

prompt flows

agent-to-agent orchestration

routing and fallback strategies

guardrails and permissions

memory and context handling

observability and control

In other words, the separation of business logic from the common plumbing every agent system needs.

As agent-based systems become more complex, teams are beginning to recognise that many of the hard problems, orchestration, safety, observability and control, are shared across use-cases. This is creating early pressure toward common runtime layers, even though the space is still evolving.

If this direction continues, building agent systems will increasingly resemble operating distributed systems, each with their own failure modes and governance requirements.

For Saratoga, this points directly to where clients need help:

designing these layers responsibly

integrating them into existing platforms

operating them safely

and making them observable and auditable

This is not tooling work. This is architecture work.

Explore: How to Implement AI Ethically in Business Solutions

Operational Agents Are Gaining Ground

Some of the most promising use-cases emerging are not customer-facing at all, but operational. Infrastructure agents, database agents, cloud compliance agents, API integration agents, DevOps and security-focused systems are all promising candidates.

These domains share something important: their outputs are verifiable artefacts. Terraform. CloudFormation. JSON schemas. SQL. Configuration files. Scripts.

Where outputs are structured and testable, AI agent performance improves dramatically. This fits closely with our own view: AI agents are strongest where verification, reproducibility and guardrails are built into the domain.

For many of our clients – particularly in cloud, finance, data and platform-heavy environments – these categories will become directly relevant.

Hardware Will Evolve, but it’s not the Main Story

There was movement on training infrastructure, and NVIDIA’s dominance is no longer uncontested. But for most teams building systems, the hardware layer remains largely background noise.

There were no major breakthroughs in low-latency inference this year. Most production workloads still rely heavily on existing GPU infrastructure.

For us, this simply reinforces where our focus belongs: architecture, integration, safety, and system design.

The hardware race will continue. Our work remains grounded in how these capabilities are responsibly applied.

What This Means for How We Work

Agentic AI is not a product upgrade. It is a shift in how work gets organised. And that changes what good consulting looks like.

For Business Consultants

Our expert business consultants’ role becomes more important, not less. AI Agent adoption is process change. It is workflow design. It is organisational design.

Our business consultants help our clients to:

rethink workflows for agent execution

define escalation paths and human hand-offs

put governance and guardrails in place

align teams around new operating patterns

ensure that AI effort translates into business value

Clear thinking, structured problem framing, and grounded stakeholder engagement will matter more than ever.

For Technical Consultants

Saratoga’s technical consultants’ focus moves further up the stack. Beyond features and services, they design the environments our clients’ agents operate inside.

This includes:

agent architectures and frameworks

planning and memory patterns

observability and safety layers

secure tool exposure

routing across models

validation of outputs

deep integration into business systems

This is where engineering discipline meets intelligent automation. It demands both.

The Foundry Hackathon

Early this year, we will run a Foundry session focused specifically on agentic systems. The challenge is practical: implement a MCP (Model Context Protocol) server that allows natural-language querying of a database.

This gives us a real platform to explore:

secure agent tool-use

expose databases safely

validate structured outputs

early agent-fabric patterns

and consultants collaborating in an agent context

It is deliberately grounded. The kind of work we expect to do for clients, not around them.

Move into the Age of AI Agents With Confidence

We are moving into a period where autonomy becomes part of everyday operating models. Not dramatic. Not theatrical. Just another gear in the machine.

Saratoga’s work sits where it always has: between human judgement, technical craft, and real business context. That intersection is exactly where agentic systems will succeed or fail.

Our job in 2026 is not to chase the noise. It is to do the work. Build the understanding. Test responsibly. Design carefully. And help our clients apply these capabilities in ways that are useful, safe and grounded in how their organisations actually function.

Enquire today.