Others agree that evaluations are critical for enterprise-grade agentic systems. “Treat agents like regulated systems,” says Gurtu. “Sandbox changes, and test agents in simulation.”
Behavioral observability
Lastly, another core layer is observability. For agentic systems, this must go beyond traditional monitoring or failure detection to capture advanced signals, such as why agents failed, or why they picked certain actions over others.
“Observability must be built in from day one,” says Sonar’s Kussberg. “You need transparency into every step of execution: prompts, tool calls, intermediate decisions, and final outputs.”
With more observable agent behaviors, you can improve the system continuously over time. As Kussberg says, “transparency fuels improvement.”
Context optimization strategies
Nearly all experts agree: giving AI agents minimal, relevant data is far better than data overload. This is critical to avoid maxing out context windows and degrading output quality.
“Thoughtful data curation matters far more than data volume,” says Brosamer. “The quality of an agent’s output is directly tied to the quality of its context.”
At Block, engineers maintain clear README files, apply consistent documentation standards and well-structured project hierarchies, and adhere to other semantic conventions that help agents surface relevant information.
“Agentic systems don’t need more data, they need the right data at the right time,” adds Sonar’s Kussberg. “Effective systems give agents versatile discovery tools and allow them to run retrieval loops until they determine they have sufficient context.”
The prevailing philosophy is to adopt progressive disclosure of information. Shopify takes this to heart, using modular instruction delivery. “Just-in-time context delivery is key,” says McNamara. “Rather than overloading the system prompt, we return relevant context alongside tool data when it’s needed.”
Others point out that context should include semantic nuances too, says Kovi. “If an agent doesn’t know ‘active users’ means something different in product versus marketing, it’ll give confident wrong answers,” she says. “That’s hard to catch.”
Architectural best practices
There are plenty of additional recommendations regarding agentic systems development. First is the realization that not everything needs to be agentified.
Pairing LLMs and MCP integrations is great for novel situations requiring highly scalable, situationally-aware reasoning and responsiveness. But MCP can be overkill for repetitive, deterministic programmed automation, especially when context is static and security is strict.
As such, Kilkommins recommends determining what behavior is adaptive versus deterministic, and codifying the latter, as this will allow agents to initiate intentionally-defined programmed behaviors, bringing more stability.
Determining the prime areas for agentic processes also comes down to finding reusable use cases. “Organizations that have successfully deployed agentic AI most often start by identifying a high-friction process,” says Ramsey. This could include employee service requests, new-hire onboarding, or customer incident response, he says.
Gurtu adds that agents perform best when they are given concrete business goals. “Start with decisions, not demos,” he says. “What doesn’t work is treating agents like stateless chatbots or replacing humans overnight,” says Gurtu.
Others believe that narrowing an agent’s autonomy yields better results. “Agents work best as specialists, not generalists,” Kussberg says.
For instance, Shopify sets clear boundaries when scaling tools. “Somewhere between 20 and 50 tools the boundaries start to blur,” says McNamara. While some propose separating role boundaries with distinct task-specific agents, Shopify has opted for a sub-agent architecture with low-level tools.
“Our recommendation is actually to avoid multi-agent architectures early,” McNamara says. We are now getting into sub-agents with the right approach, and one key principle is to build very low-level tools and teach the system to translate natural language to that low-level language, rather than building out tools scenario by scenario.”
Experts share other wisdom for designing and developing agentic systems:
- Use open infrastructure: Open agents and vendor-agnostic frameworks allow you to use the best fit-for-purpose models.
- Think API-first: Good API design and clear, machine-readable definitions better prepare an organization for AI agents.
- Keep data in sync: Keeping shared data in sync is another challenge. Event-driven architectures can keep data fresh.
- Balance access with control: Keeping agentic systems secure will require offensive security exercises, comprehensive audit logs, and defensive data validation.
- Continually improve: To avoid agent drift, agentic systems development will inevitably require ongoing maintenance as the industry and AI technology evolve.
The future for agentic systems
Agentic AI development has moved forward at a blistering pace. Now, we’re at the point where agentic system patterns are beginning to solidify.
Looking to the future, experts anticipate a turn toward more multi-agent systems development, guiding the need for more complex orchestration patterns and reliance upon open standards. Some forecast a substantial overhaul to knowledge work at large.
“I expect that in 2026, we will see experimentation with frameworks to structure ‘factories’ of agents to coordinate producing complex knowledge work, starting with coding,” says Block’s Brosamer. The most challenging aspect will be optimizing existing information flows for agentic use cases, she adds.
One aspect of that future could be more emphasis on alternative clouds and edge-based inference to move certain workloads out of centralized cloud architecture to reduce latency.
“The future of competitive AI demands proximity, not just processing power,” says Akamai’s Weil. “Agents need to act in the real world, interacting with users, devices, and data as events unfold.”
All in all, building agentic systems is a highly complex endeavor, and the practices are still maturing. It will take a combination of novel technologies, microservices-esque design thinking, and security guardrails to take these projects to fruition at scale in a meaningful and sustainable way — all while still granting agents meaningful autonomy.
The future looks agentic. But the smart system design underpinning agentic systems will set apart successful outcomes from failed pilots.
