Behind the Curtain: How Agentic AI Systems Are Really Built
As the tech world races toward more autonomous and adaptive AI, agentic AI—systems capable of reasoning, planning, and taking actions—has moved from research labs into real-world production. But while the promise is enormous, practitioners say the path to building these systems is far less glamorous than the headlines suggest.
A recent session titled “Behind the Curtain: Building Agentic AI Systems in Real-World Projects” pulled back the veil, offering candid lessons from teams deploying agentic AI at scale. The takeaway was clear: success hinges not just on powerful models, but on the unglamorous engineering and data work that surrounds them.
Robustness Is Harder Than Intelligence
Practitioners stressed that the biggest challenge is not giving an AI agent the ability to think — but ensuring it behaves reliably every time. Production-grade agentic systems demand:
- Strong guardrails and fallback logic
- Task-decomposition techniques that prevent runaway reasoning
- Deterministic workflows for inherently probabilistic models
- Tight integration with traditional software components
For example, one team described the debugging process as “building a distributed system where the nodes sometimes hallucinate.” Ensuring consistency across tasks—especially in complex environments such as finance, logistics, or customer operations—requires rigorous system design, not just clever prompt engineering.
Evaluation: The Silent Challenge
Unlike classical machine learning, where metrics are well-defined and repeatable, evaluating agentic AI can feel like chasing a moving target.
Speakers highlighted two issues:
- Outcome diversity – Agents can complete tasks in multiple valid ways, making accuracy hard to quantify.
- Hidden failure-modes – Small errors compound across multi-step reasoning, producing failures that are difficult to trace back.
Teams increasingly rely on simulation environments, synthetic test-cases, meta-evaluation models, and continuous monitoring to assess quality. But even with these, evaluation remains one of the biggest blockers to broader agentic AI adoption.
As one expert summarised: “Agents don’t fail loudly—they fail subtly.”
Data Quality: The Real Differentiator
While models get most of the attention, practitioners argued that the true driver of successful agentic AI isn’t the algorithm—it’s the data ecosystem around it.
High-impact deployments rely on:
- Clean, unified knowledge bases
- Real-time access to operational data
- Structured APIs and tools for agents to act reliably
- Domain-specific corpora for grounding decision-making
In fact, teams noted that improving data quality often yielded bigger performance gains than swapping out model versions. Or as one engineer put it: “If your data is messy, your agent will be messy.”
Where Agentic AI Goes Next
Despite the complexity, the momentum is undeniable. Early deployments are already transforming customer service, fraud detection, software operations, and enterprise automation. As agent frameworks mature and evaluation tooling improves, the industry expects agentic systems to shift from experimental prototypes to mission-critical infrastructure.
The future vision is not just AI that responds—but AI that collaborates, plans, executes, and adapts in real time.
And as this session made clear, the real breakthroughs will come not simply from bigger models, but from better engineering and better data.
Selected Resources for Further Deep-Dive
To support your own work or study in agentic AI (especially given your strong background in AI, ML, system development and productionisation), here are carefully selected resources:
| Resource | What you’ll learn | Why it matters |
|---|---|---|
| McKinsey & Company – “One Year of Agentic AI: Six Lessons from the People Doing the Work” (McKinsey & Company) | Real-world cases: 50+ agentic AI builds, pitfalls, workflows. | Gives concrete industry evidence of what works (and what doesn’t). |
| Boston Consulting Group (BCG) – “How Agentic AI is Transforming Enterprise Platforms” (Boston Consulting Group) | Quantitative impact (30-50% acceleration), architecture implications. | Helps assess business value and enterprise transformation angles. |
| Anthropic – “Building Effective Agents” (research page) (Anthropic) | Technical building blocks: LLM + retrieval + tools + memory, interface design. | Excellent for understanding the guts of agentic systems and production readiness. |
| FreeCodeCamp – “The Agentic AI Handbook: A Beginner’s Guide” (FreeCodeCamp) | Step-by-step tutorial, Python code, practical implementation tips. | Great for hands-on experimentation—fits your developer background. |
| IBM – “Agentic AI: 4 Reasons Why It’s the Next Big Thing” (IBM) | Conceptual framing, what distinguishes agentic vs traditional AI. | Useful for strategic planning and positioning when designing your systems. |
| GovTech Singapore – “Agentic AI Primer” (government developer portal) (developer.tech.gov.sg) | Standards, best practices, Singapore/ASEAN-relevant guidelines. | Especially helpful given your location in Singapore and for compliance/regulatory view. |
| Academic survey papers: – “Agentic AI: A Comprehensive Survey of Architectures, Applications & Future Directions” (arXiv) – “Agentic AI Frameworks: Architectures, Protocols & Design Challenges” (arXiv) – “TRiSM for Agentic AI: Trust, Risk & Security Management” (arXiv) | In-depth, cutting-edge research on agentic AI frameworks, risk, governance, protocols. | Valuable for designing your systems with a strong research/robustness/architecture foundation. |
Relevance to Your Experience & Systems
Given your strong background (20+ years in R&D, AI/ML, system development, enterprise integration) and your current projects (email automation, trading system, HR/ERP features), here are some tailored reflections:
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System robustness: Much of the guidance emphasises that building agentic AI isn’t about dropping in a large language model—but integrating it into workflow, tooling, data pipelines, monitoring and continuous feedback loops. This aligns with your systems engineering mindset.
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Data pipeline discipline: Your experience with metadata extraction (taxi invoices), email summarisation, intelligent routing—all are relevant. The emphasis in practice is on clean data, correct context and strong integration with the “action” side of agents (APIs, tool-calls). The resource about “Garbage in, Agentic out” underlines this. (TechRadar)
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Evaluation & monitoring infrastructure: Since part of your interest is building production systems (e.g., your email-processing app with FastAPI, Celery, etc.), you’ll find the challenges of measuring multi-step agents particularly relevant. The research on TRiSM and operational monitoring will help.
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Enterprise context & governance: Your interest in intelligent ERP/email assistant and trading platform means you’re working in mission-critical domains. The sources above (HBR article on designing agentic systems (Harvard Business Review)) emphasise cross-functional execution and embedding controls. Good to keep top-of-mind.
Final Thoughts
Building real-world agentic AI is not just about flashy agents with autonomy—it’s about disciplined engineering, data hygiene, evaluation strategy, monitoring, and embedding into existing workflows and systems. For you, Sheng, this means: leverage your depth in system design and AI, prioritise robustness and data quality early, build your monitoring/evaluation stack as you build the agent, and treat the agent as part of a larger system (not a standalone LLM magic bullet).
If you like, I can pull together a reading-pack (PDFs + code links) of 10 of the best resources for agentic AI system building in production (including open-source frameworks, architecture patterns, case studies). Would you like that?
