10 Do’s and Don’ts for Building Agentic Applications
Agentic AI is rapidly gaining traction, but building reliable, scalable agents still feels like navigating uncharted territory. Across countless online discussions, developers consistently report the same pitfalls, such as oversized frameworks, opaque failures, non-deterministic behavior, and unpredictable costs. If you’re building agentic applications for serious workflows, here are the ten do’s and don’ts every team should follow based on the lived experiences of engineers building these systems today.
- 1. DON’T over-engineer on day one
- 2. DON’T think of AI agents as black boxes
- 3. DON’T assume AI agent behavior will be consistent out of the box
- 4. DO design for model-agnostic behavior
- 5. DO validate, chunk, and clean inputs
- 6. DON’T assume agent systems “just work”
- 7. DO choose infrastructure that survives growth
- 8. DON’T hide the reality of AI agent behavior from stakeholders
- 9. DON’T Ignore LLM-Specific Failure Modes
- 10. DO define the workflow before writing a prompt
- 11. Final thoughts
Key takeaways
- Start small with explicit workflows before adopting complex agent frameworks.
- Prioritise observability early to avoid opaque, hard-to-debug failures.
- Build repeatable tests because agent behaviour is inherently non-deterministic.
- Use model-agnostic designs to prevent vendor lock-in and inflexibility.
- Clean, validate, and structure data before feeding it to agents.
- Coordinate multi-agent workflows explicitly to avoid loops and inconsistent outputs.
- Choose scalable infrastructure early, not temporary convenience platforms.
- Set clear expectations by openly communicating agent limits and costs.
- Monitor for common LLM failure modes like loops, drift, and misuse.
- Define the agent’s mission upfront to avoid wasted development effort.
1. DON’T over-engineer on day one
When teams first jump into agentic AI, the natural instinct is to reach for large projects that promise end-to-end automation. But most early projects don’t need any of that, because this approach leads to slower delivery, higher maintenance, and more mistakes along the way.
The starting point should be simple, so you are in full control. For example, you can design an AI agent that can take user input → Run a prompt → Call a tool or access a database → Validate → Return result. This is more or less how our engineers approached building an agent for an AI LegalTech app.
This process is easy to find shortcomings, easy to scale, and easy for everyone to understand. Once you know exactly where the bottlenecks, complexity, or orchestration needs appear, then introduce more complexity. By starting simple, you minimise surprises and build a foundation you can scale later.
2. DON’T think of AI agents as black boxes
A common trap teams fall into is treating AI agents like mysterious “black boxes” that take input and magically decide what to do. But under the hood, an agent is just a chain of decisions: reasoning steps, tool calls, memory retrieval, returning output, and any one of these can fail silently.
It’s like running a distributed system with zero logs. You know something went wrong, but you have no idea whether it was the API call, data fetch, business logic, or config. For agentic AI systems, you can’t tell why something happened unless you have an observability tool like an Arize Phoenix that we like to use, so you can:
- Log every prompt and model response (with redaction rules if needed)
- Track token usage per request and per agent step
- Record tool decisions and outcomes
- Capture intermediate reasoning states
- Add trace IDs for multi-step workflows
- Surface failures with clear, searchable logs
This turns the agent from a black box into a transparent, inspectable system where you can see every step of the chain. Observability is the foundation that keeps your agent’s behaviour explainable, debuggable, and trustworthy as it scales.
3. DON’T assume AI agent behavior will be consistent out of the box
The next point connects to the previous point: agentic AI won’t give you consistent outputs.
One of the biggest mental shifts for traditional software engineers is realizing that AI agents are not deterministic systems. With normal code, the same input produces the same output every time. With LLMs, the opposite is true: even a perfectly valid prompt can produce different reasoning paths, different tool selections, or slightly different interpretations on every run. This unpredictability doesn’t mean your system is flawed. It’s simply how probabilistic models behave. But it does mean that you must treat agents as systems that can drift, regress, or fail in subtle ways that aren’t obvious at first glance.
For example:
- The agent might decide to call a tool on one run but not on another.
- It may follow a reasoning chain that leads to a different intermediate conclusion.
- A tiny change in context, such as different document order, can shift the output.
- The model might misinterpret a response schema it previously handled correctly.
The fix for this that help you mitigate the risk is to design for determinism where possible and test variability where you can’t avoid it. Start with simple pipelines where the outputs are easy to evaluate—such as document → summary → email.
Then create a test harness that checks the parts of the system that should remain stable:
- Output schemas follow expected structure
- Tool choices align with the workflow logic
- Retry mechanisms work as designed
- The agent respects constraints and guardrails
- Failures produce consistent, interpretable error states
You’re not testing for a word-for-word identical output. You’re testing for behavioral consistency under controlled inputs. Think of it like automated testing for a distributed system that can react differently each run. You can’t make it fully deterministic, but you can make it predictable enough to detect real regressions, measure drift, and maintain reliability over time.
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4. DO design for model-agnostic behavior
A common trap in agentic AI development is tightly coupling your system to a single LLM provider. Teams often optimise prompts, tool integrations, or reasoning workflows specifically for one model’s behaviour. While this can accelerate early development, it creates a long-term dependency: if the vendor changes APIs, pricing, or model behaviour, your system may break or require extensive rewrites. Think of it like hardcoding a single cloud service for critical infrastructure. Today it works perfectly, but tomorrow a minor version change, rate-limit adjustment, or cost hike can force an expensive migration. The business impact is reduced flexibility and vendor risk, resources are wasted on costly rewrites, and operational performance may drift when a new model behaves differently.
The solution is to design your system to be model-agnostic, building abstractions that separate workflow logic from the specific LLM. Wrapping LLM calls behind an interface, parameterising prompts and templates, and testing multiple providers early ensures that outputs remain consistent regardless of the underlying model. This approach future-proofs the system, reduces vendor lock-in, and allows teams to switch models or optimise for cost, performance, or compliance without rewriting the entire agent.
5. DO validate, chunk, and clean inputs
A common mistake in agentic AI development is feeding raw or unprocessed data directly into agents. Developers often assume the model can handle messy or inconsistent inputs, but in practice, data preparation is the most resource-intensive part of building reliable agents. Unvalidated inputs can introduce bias, expose sensitive information, or create compliance risks. Operationally, large or unchunked data can exceed token limits, break workflows, or cause the agent to produce incomplete or incorrect outputs. To prevent these issues, it’s essential to validate data schemas, clean and anonymize sensitive information, and break larger datasets into manageable chunks or summarized segments. This ensures the agent processes reliable, structured inputs, reducing errors and making downstream reasoning and tool usage far more predictable and efficient.
6. DON’T assume agent systems “just work”
When talking about multi-agent systems, it might seem like multiple agents running independently, but in reality, they require careful orchestration to function correctly. Without explicit workflow coordination, agents can produce inconsistent outputs, get stuck in loops, or create conflicts when passing information between each other. This not only reduces trust in the system’s reliability but also increases the effort needed to manage and debug interactions. To prevent these issues, design your architecture with structured coordination mechanisms—such as state machines or orchestrators—that explicitly manage task handoffs, data sharing, and verification between agents. Clear workflows ensure that each agent’s output aligns with the system’s overall objectives, maintaining stability and predictable performance across the entire multi-agent environment.
7. DO choose infrastructure that survives growth
Relying on no-code or low-code platforms, or prebuilt convenience solutions, can speed up initial prototyping. However, as agents evolve from simple prompt chains into autonomous systems that call tools, coordinate with other agents, and operate continuously, infrastructure becomes the limiting factor. Hidden execution traces, restricted customization, opaque cost structures, and platform lock-in can turn early convenience into long-term technical debt.
Designing for growth means planning for autonomy, observability, and control from day one. Agentic systems require full execution tracing, so every prompt, tool call, decision, and memory write must be inspectable. They require modular, containerized deployment patterns that allow independent scaling of reasoning, tools, and memory layers. They require infrastructure as code for reproducibility, role-based access controls for governance, and vendor-agnostic abstractions to avoid lock-in. Most importantly, they require guardrails: spending limits, loop detection, failover mechanisms, and human-in-the-loop controls. The difference between a compelling demo and a production-grade agent system is infrastructure that survives growth.
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8. DON’T hide the reality of AI agent behavior from stakeholders
Agentic AI systems are powerful, but they are not yet fully autonomous. Expecting them to behave like human employees or “magical” problem solvers sets teams up for disappointment. Clear communication about capabilities, limitations, and potential failure modes is critical to maintain trust and ensure realistic planning.
Why it matters:
- Stakeholders may overestimate what agents can do
- Misaligned expectations lead to dissatisfaction and project delays
- Latency or cost spikes can surprise teams if unmonitored
- Misunderstanding agent limitations can result in operational mistakes
- Engineers spend unnecessary time clarifying misunderstandings
Best practices for transparent communication:
- Document agent capabilities and boundaries clearly
- Provide realistic cost and performance estimates
- Outline failure modes and potential points of error
- Include expected variance in outputs for LLM-driven tasks
- Maintain a communication channel for reporting unexpected behavior
- Educate users and stakeholders on the agent’s level of autonomy
- Include human-in-the-loop checkpoints in high-risk operations
- Highlight assumptions made by agents during reasoning or tool use
- Update documentation and dashboards as agents evolve
9. DON’T Ignore LLM-Specific Failure Modes
Large Language Models (LLMs) underpin many agentic AI systems, but they have distinct failure modes that teams often overlook. These issues are subtle, yet they can significantly reduce trust and reliability. Common problems include looping reasoning, where the agent repeats the same steps without progress; prompt drift, where slight changes in input cause unpredictable outputs; incorrect tool usage, where the agent calls the wrong function or misinterprets API requirements; and inconsistent outputs, where the same input generates different results across runs.
These failures impact business operations by eroding confidence in the system, delaying processes, and introducing potential errors. From an operational perspective, missing tool calls or misinterpreted data can halt workflows or propagate mistakes downstream. Resources are also affected: engineers can spend hours debugging loops, analyzing drift, and correcting unexpected outputs.
To manage these risks, teams should implement step-by-step monitoring and evaluations. Observability tools can track each reasoning step, tool call, and intermediate output, making it easier to detect anomalies early. The table below summarizes the key LLM-specific failure modes and their effects:
| Failure mode | What does it mean? | What’s the impact? |
|---|---|---|
| Looping reasoning | Agent repeats steps without progress | Wastes resources, delays processes |
| Prompt drift | Small input changes produce unexpected outputs | Inconsistent results, unreliable predictions |
| Incorrect tool usage | Calls wrong function or misinterprets API | Fails workflows, introduces errors |
| Inconsistent outputs | Same input generates different responses | Difficult QA, reduces stakeholder trust |
Managing LLM-specific risks requires system-level safeguards, not just better prompts. Teams should implement:
- Step-level observability (what we’ve said here before)
- Structured output validation and schema enforcement
- Loop detection and automatic termination thresholds
- Deterministic settings where appropriate (e.g., temperature controls)
- Evaluation pipelines that test edge cases and drift scenarios
- Human-in-the-loop checkpoints for high-risk actions
10. DO define the workflow before writing a prompt
Starting agentic AI development without a clear mission is like sending a team of specialists into a city without a map: they may work hard, but their efforts won’t converge on the intended destination. Practitioners frequently note that unclear goals lead to wasted engineering hours, unpredictable outputs, and difficulty measuring success.
A better approach is to think of your agent not just as a model that responds to prompts, but as a small, orchestrated workflow engine.
Begin by defining the agent’s ultimate mission: what problem it solves, who it serves, and what constitutes success. Then, break that mission into a series of logical, verifiable steps. Each step should have a purpose, inputs, expected outputs, and checkpoints to ensure correctness.
Instead of launching directly into prompt engineering, visualize the agent’s workflow as a mini roadmap. This roadmap guides prompt creation, testing, and debugging, aligning every step of the agent’s reasoning with measurable objectives. By designing the mission first and prompts second, teams can ensure that agents don’t just produce output. They execute meaningful, reliable tasks that add real value.
11. Final thoughts
Agentic AI is powerful, but still maturing. Stories shared across online communities show that systems fail not because the LLM is weak, but because workflow design, observability, and expectations are often missing or inconsistent. By following these 10 Do’s & Don’ts, your agentic applications will become easier to debug, more predictable, safer to scale, cheaper to operate, and more reliable for business-critical workflows.
If you’re looking to build agent-first applications that are robust, scalable, and aligned with your business goals, our team at DAC.digital can help. We combine AI engineering, software development, UX design, and DevOps expertise to design and implement agentic AI systems that integrate seamlessly with your existing workflows, maintain full observability, and deliver measurable impact. Reach out to explore how we can help you build reliable, production-ready agents tailored to your organization’s needs.
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