The AI Agent Tech Stack in 2025: What You Actually Need to Build & Scale

AI agents have moved from futuristic prototypes to working components in modern enterprises. Companies aren’t asking if they should adopt agentic systems—they’re asking how to do it in a way that delivers real value. Whether you're optimizing internal workflows, upgrading customer service, or accelerating research, the promise is clear: reduce manual work, improve decisions, and boost productivity.

But behind the promise is a harsh reality: most organizations underestimate what it actually takes to build agents that work in production. The tooling landscape is fragmented. Frameworks vary wildly in abstraction. Monitoring, compliance, and performance tuning are often treated as afterthoughts.

We know—because we built our own AI agent.

And yet, the internet is flooded with ads claiming you can “build your own AI agent without any tech knowledge”—as if dropping a few components means you’re ready for production use. These tools might help you build something, but that doesn’t mean it will work—let alone drive meaningful business results.

At Netguru, we developed an internal AI agent called Omega to support our sales team. Designed to automate repetitive tasks, deliver contextual insights, and guide team members through our Sales Framework, Omega evolved from a quick proof of concept into a daily-use Slack-native assistant.

To get there, we had to make critical decisions about the agent tech stack—from orchestration frameworks and memory handling to observability tools and red teaming strategies. This article is a breakdown of those decisions:

• What we actually used
• What we tested and rejected
• What we’d recommend if you’re building an AI agent in 2025

Whether you're a CTO, innovation lead, or product strategist, this guide is your shortcut past the hype—and toward building something that actually works.

2. The Problem: Building an Agent That Actually Works

Building an AI agent isn’t just a technical challenge—it’s an operational one. It’s not enough for an agent to respond intelligently in a sandbox; it needs to integrate into the reality of your team's daily work, handle edge cases, and stay useful over time.

For our sales team, the day-to-day was filled with tool-switching and manual updates. Slack, HubSpot, Google Drive, and Salesforce all held critical pieces of context—but no single system connected them. Reps often relied on memory or scattered notes to prepare for calls, track deal momentum, or follow our internal Sales Framework. The process was slow, fragmented, and prone to inconsistency.

We didn’t need an agent that could chat—we needed one that could work alongside humans, inside our workflows.

To deliver that, our agent needed to:

• Integrate natively with Slack, Google Drive, HubSpot, and Salesforce
• Follow the structure of our Sales Framework without manual enforcement
• Be modular and easy to evolve as needs changed
• Offer full traceability, cost monitoring, and feedback capture
• Allow for real-time adjustments and performance improvements

These requirements set the bar high—and made it clear that selecting the right tech stack would determine whether our agent stayed a demo… or became a daily driver.

3. Inside a Real Decision: Why We Chose AutoGen + AgentChat

When we set out to build Omega, we weren’t just creating a single-use bot—we were laying the foundation for multi-agent systems that could eventually support a range of use cases. Beyond sales, we envisioned creative collaboration flows, research automation, and dynamic briefing—all happening across agents working in tandem within Slack.

Choosing the right framework became a pivotal decision. It had to support:

• Structured agent interactions (e.g. brief builder ↔ researcher ↔ director)
• Seamless integration with our internal tools and APIs
• Future extensibility for other domains and roles

We evaluated several leading options:

• OpenAI Agents – lightweight but too low-level, with limited abstraction for managing agent collaboration
• Google ADK – promising in concept, but too immature at the time (only days old, already dozens of open issues)
• AgentChat – easy to use, but too opinionated and limiting on its own
• AutoGen – a layered, extensible framework built for serious multi-agent engineering

Why We Started With AgentChat—And Why It Makes Sense for Most Teams

Ultimately, we chose AutoGen, but we didn’t start with its low-level core. Instead, we began with AgentChat, a high-level interface built on top of AutoGen that helped us move fast while staying flexible.

If you’re building a prototype or internal tool, AgentChat is a smart entry point. It lets you develop quickly using a clean, simplified API—without boxing you in. Once your use case matures or requires more control, you can drop into AutoGen Core to fine-tune behaviors and system architecture.

This layered approach allowed us to get a working version of Omega into Slack in just a few days, then evolve it over time—without hitting limitations or needing to rebuild from scratch.

Why AutoGen Was the Right Fit for Us

Starting with AgentChat gave us a fast, low-friction way to get Omega up and running. But as our use case grew in complexity, we needed more than just a high-level API—we needed a robust foundation that could scale with us.

That’s where AutoGen stood out.

After testing and comparing our options, AutoGen emerged as the framework that struck the right balance between flexibility, maturity, and ecosystem depth. Some tools were simpler to prototype with. Others promised lightweight setups. But few could meet the demands of a real-world, multi-agent system like Omega—where reliability, extensibility, and integration with internal infrastructure were non-negotiable.

AutoGen’s layered architecture was key. It let us start simple with AgentChat, then gradually transition to AutoGen Core for deeper customization and control. We didn’t have to over-engineer upfront, but we also didn’t have to rebuild when things got more complex.

Just as important, AutoGen aligned with our technical and compliance requirements. It supported secure, private LLM deployments via Azure—fitting neatly into our existing infrastructure. And its growing ecosystem (tool calling, retrieval, browser agents, community support) gave us confidence that we were investing in more than a tool—we were building on a foundation that would last.

You can see how it stacked up against other frameworks in our full evaluation below:

Framework Comparison: What We Evaluated Before Choosing AutoGen

A Quick Note on AutoGen

Many frameworks focus on just one piece of the puzzle—either orchestration or agent logic. AutoGen, by contrast, has evolved into a full-fledged ecosystem.

It now includes:

• AutoGen Core and AgentChat for layered development
• AutoGen Studio for no-code prototyping
• AutoGen Bench for performance benchmarking
• An Extensions API that supports integrations with LangChain, GraphRAG, MCP, and more

This makes AutoGen one of the most complete, flexible, and production-ready agent platforms available in 2025. Whether you're building for internal automation or customer-facing use cases, it gives your team the tools to scale without getting boxed in.

4. The Core Stack: What You Actually Need

TL;DR – What You Need in an Agent Stack (2025):
Use a reliable language model like Azure OpenAI’s GPT-4o or reasoning model like o3-mini, orchestrate agents using a flexible framework like AutoGen, persist memory with structured context and vector databases, and give agents real tools—like web browsing or API access. This setup scales from proof of concept to production without lock-in.

Language Model: Enterprise-Grade and Flexible

At Omega’s foundation is o3-mini, served through Azure OpenAI. This gave us enterprise compliance, low latency, and future-proof flexibility. While o3-mini performed well out of the box, we chose AutoGen in part for its model-agnostic design—letting us swap in fallback models or fine-tuned variants down the road.

Importantly, o3-mini belongs to a new class of reasoning models—LLMs trained with reinforcement learning to think before they answer. These models produce a long internal chain of thought, making them ideal for complex problem solving, multi-step planning, and agentic workflows. Reasoning models like o3 and o4-mini are particularly well suited for use cases involving tools like Codex CLI, or anywhere agents need to "think out loud" before acting.

🔧 Tech Highlight:
GPT o3-mini via Azure, fully integrated with AutoGen.
Reasoning-optimized model for agentic use cases.

Orchestration: Managing Multi-Agent Workflows

To make Omega more than just a single-task bot, we leaned on AutoGen’s orchestration features—specifically, its RoundRobinGroupChat. This structure allowed agents to “take turns” logically within a shared Slack channel (e.g., researcher → brief generator → tone adjuster), mimicking real-world collaboration.

AutoGen also handled dynamic context passing: voice guidelines, project metadata, and channel history traveled with the task, enabling cohesive agent behavior across steps.

As we expand Omega’s capabilities, we’re moving toward a more intelligent orchestration strategy using SelectorGroupChat. Instead of rigid turn-taking, this setup lets the AI decide which agent should act next—based on task relevance, context, or confidence. It’s a step toward more autonomous, adaptive multi-agent workflows.

🔧 Tech Highlight:
Structured collaboration via RoundRobinGroupChat, with smart stop conditions for cleaner interactions.
Next: upgrading to SelectorGroupChat for intelligent agent coordination

Memory & State: Scoped, Structured, and Searchable

Agents are only useful if they remember context—but that doesn’t mean you need a vector database to get started.

For Omega, we implemented scoped memory tied to Slack threads, capturing metadata like tone, brand context, and prior actions. This allowed the agent to resume conversations without retraining or global memory structures.

While many agentic stacks default to vector databases, we found it far more pragmatic to let the agent query real-time APIs instead. Our systems already exposed the right data—no need to embed everything upfront.

🔧 Tech Highlight:
Scoped thread memory in Slack + context fetching via existing APIs (no vector DB required).

Tool Use

Omega’s agents weren’t just chat responders—they executed real tasks by calling tools.

Using AutoGen’s Extensions API, we wired up a practical starter kit:

• Slack feed – getting and searching recent messages
• Google Drive reader – walking folders, parsing docs
• Apollo API – pulling crisp company intel, no scrapers needed

This lightweight toolkit gave agents the ability to summarize notes, find documents, track deal momentum, and support sales workflows—without human hand-holding.

🔧 Tech Highlight:
Extensions API
Custom and third-party tools connected—turning Omega into an actual teammate.

5. Testing, Observability & Guardrails

Building an AI agent that works isn’t enough. Once AI is integrated into daily workflows, it must be observable, continuously evaluated, and resilient to misuse or failure. Without that foundation, even the most capable agent becomes a liability.

From day one, we prioritized full transparency—tracking every input, output, decision, and cost across Omega’s lifecycle.

End-to-End Tracing with Langfuse

To debug, improve, and scale Omega, we integrated Langfuse to capture the entire lifecycle of every interaction.

Each trace includes:

• The full input prompt and system messages
• The LLM’s output
• Token usage, latency, and cost
• Intermediate steps in a multi-agent flow
• Agent graphs and session context across conversations

This level of observability made it easier to detect hallucinations, understand where context was lost, and replay exact scenarios during debugging.

Tech Highlight: Langfuse Tracing
Production-grade observability with full trace replays, token/cost monitoring, and multi-turn agent context.

Prompt and Output Evaluation

We used Langfuse’s built-in evaluation tools and paired them with Promptfoo to test and benchmark prompt behavior.

• In dev: we tested prompts across scenarios using real data and simulated inputs
• In production: we collected live feedback (thumbs up/down) and flagged low-quality responses
• Over time: we ran structured evaluations to compare prompt iterations or model switches

This feedback loop was critical for improving precision and avoiding regressions.

Pro tip: Langfuse also supports dataset-based evaluations and LLM-as-a-judge scoring—great for agents expected to handle nuanced outputs (like summaries or tone matching).

Red Teaming & Security Testing with Promptfoo

Security wasn’t just a checkbox—it was baked into our testing pipeline.

We used Promptfoo’s red teaming mode to simulate:

• Direct and indirect prompt injections
• Jailbreak attempts
• Toxic content or unauthorized tool execution
• Potential PII leakage or misuse of sensitive input

This helped us harden Omega’s behavior early—especially for Slack-connected agents with access to internal documents.

Self-Learning Guardrails

Rather than relying only on static filters, we took an adaptive approach:

• Guardrails evolved based on red team findings
• Prompts were rewritten or abstracted to reduce exploitability
• Failure modes informed future prompt structure and tooling limits

This created a learning loop where security and reliability improved over time, rather than requiring large manual audits.

Compliance Monitoring & Transparency

To align with evolving AI governance standards, we adopted compliance-friendly tooling:

• Promptfoo and Langfuse both support OWASP Top 10 for LLMs
• Token cost, model versioning, and output categories were traceable for audit logs

This prepared Omega to scale across other departments with confidence—and positioned us well for future AI audits or regulations.

6. What’s Overhyped or Still Fragile

As AI agents rise in popularity, the surrounding ecosystem is full of bold claims and fast-moving experiments. But not everything in the agent landscape is ready for production—and some of the loudest trends come with real risks if you’re building for business-critical workflows.

Here’s what we found to be more hype than reality—at least for now.

Autonomous Agent Loops (AutoGPT-Style)

The idea of fully autonomous agents that can plan, execute, and evaluate entire projects without human oversight is still more aspirational than practical. In our testing, AutoGPT-style agents frequently got stuck in redundant task loops, drifted off track, or produced irrelevant outputs.

The core issues: poor grounding, weak memory discipline, and missing termination logic.

What proved more effective was designing narrowly scoped, role-specific agents with clear responsibilities and structured handoffs. It delivered more reliable results with far less risk.

Immature or Overhyped Frameworks

Some agent frameworks generate a lot of buzz early on but don’t hold up in production settings. For example, when we evaluated Google’s ADK, it had only just launched and already showed dozens of unresolved issues.

It had potential, but wasn’t mature enough for real-world deployment—especially in environments that demand compliance, reliability, and scalability.

Innovation is important. But if you're building for production, stability and support matter more than hype.

Abstractions That Obscure Behavior

Frameworks that prioritize ease of use sometimes do so at the cost of transparency. Over-abstraction can make it harder to understand, test, and control agent behavior—especially when debugging issues in production.

We chose AutoGen because it strikes the right balance. It lets you move quickly with AgentChat, but offers full access to the lower-level Core when deeper control is needed.

A helpful rule of thumb: the faster a tool helps you launch something in five minutes, the harder it may be to debug after five weeks.

7. Future-Proofing: What to Bet On in 2025

If you're building AI agents today, you're not just solving for a current use case—you're laying a foundation for systems that will evolve with your business. Tech moves fast, but some bets are safer than others. Based on our experience building Omega and evaluating dozens of tools, here’s what we believe is worth investing in.

Modular, Layered Architectures

The stack that wins is one that lets you start simple and scale complexity over time. AutoGen gave us a layered approach: AgentChat for quick wins, Core for customization, and Extensions for tooling. This modularity made Omega flexible enough to support both early prototyping and more sophisticated behaviors later.

Avoid tools that force you into a rigid pipeline or “one-size-fits-all” model. The most resilient stacks are the ones you can rewire without starting from scratch.

Ecosystem Maturity Over Hype

A thriving ecosystem isn’t just about GitHub stars—it’s about active AI development, extensibility, and a reliable feedback loop. We chose platforms like Langfuse and Promptfoo not just for their features, but because they integrate well with other tools, are frequently updated, and come with strong communities.

Choose ecosystems where:

• Bugs get fixed fast
• Docs are clear and open
• You can plug in your own logic, models, or evaluation methods

Observability and Evaluation Built In

Agents that you can’t debug are agents you can’t trust. From day one, bake in tools that let you trace behavior, capture user feedback, and evaluate changes safely. This will pay off tenfold when your agent starts handling complex tasks—or when it fails and you need to know why.

Langfuse and Promptfoo gave us full visibility across cost, performance, quality, and regressions—and they continue to evolve alongside our needs.

Governance and Compliance Awareness

The AI landscape is heading toward heavier regulation. Whether it's the EU AI Act, OWASP for LLMs, or internal governance standards, future-proofing means being audit-ready.

Start by:

• Logging everything (inputs, outputs, model versions, costs)
• Making decisions explainable
• Keeping a traceable version history for prompts, agents, and changes

Tools like Promptfoo make compliance scanning part of your dev cycle—not a painful afterthought.

Flexible Model Strategy

Don’t bet everything on a single model. Whether you’re using GPT-4o via Azure, exploring Claude, or integrating open-source models for specific tasks, your stack should allow model switching, fallback strategies, and mixed-provider workflows.

Model agility reduces cost, mitigates lock-in risk, and makes you more resilient to market shifts.

8. Final Word: Choose for Control, Not Hype

AI agents can feel magical when they work—but in reality, what powers them is well-orchestrated engineering, not buzzwords.

Behind Omega’s helpful Slack messages and smart document suggestions is a pipeline of intentional choices: a modular framework, traceable prompts, adaptable agents, and observable behavior at every step. That’s what makes it reliable. That’s what makes it scalable.

That said, the journey hasn’t been perfect. We ran into edge cases, had to redesign agent logic, troubleshoot hallucinations, and rethink prompt structures. There were moments when the agent worked beautifully—and others when it fumbled or needed human backup. Building Omega taught us that creating a dependable agent means more than just stitching tools together; it requires real learning, iteration, and patience.

Today, Omega already supports some sales tasks—onboarding new reps into active opportunities, guiding them with relevant content, and surfacing context from across internal systems. But there are still gaps. Areas like effort estimation and referencing past projects at scale are still in development and will require deeper integrations with platforms like Salesforce and HubSpot.

If you’re building an AI agent in 2025, the real differentiator isn’t which LLM you choose or how many agents you spin up—it’s how much control you retain as your system evolves.

So here’s our advice:

• Trace everything. You can’t improve what you can’t see.
• Build iteratively. Start high-level, but ensure you can dive deeper when needed.
• Stay modular. Plug-ins fail. Ecosystems change. Swappable parts are safer.
• Observe and adapt. Real-world agents need monitoring and feedback loops—baked in, not bolted on.
• Be honest about limitations. Success isn’t about perfection—it’s about steady, informed improvement.

In the end, building a real AI agent isn’t about chasing intelligence—it’s about earning understanding, through every tested prompt, every observed failure, and every choice you’re willing to own.