## Observability and Control

Given their differences in autonomy, workflows and agents also differ in how observable and controllable they are during runtime, and what mechanisms exist to supervise or intervene in their operation.

With **workflow systems**, observability is typically high because the pipeline is explicit. A developer can insert logging at each step to record the prompt given to the LLM, the LLM’s output, and any decision made by the orchestration code. Since each step is known, one can monitor the system’s state in a very granular way. For example, if there is a routing step, the system can log which route was taken for each input. If there is a verification step, the system can log whether the output passed or failed the check. This makes debugging straightforward – by examining the logs, you can pinpoint which step caused a failure or produced an unexpected result. Control is also straightforward: the developer can change what happens at a given step or add new conditions relatively easily by modifying the code. Essentially, the developer (or ops team) has a **tight control loop** around the LLM: the code “wraps” around every call to ensure it behaves as expected.

Workflows allow for **programmatic guardrails**. For instance, if an intermediate LLM output is detected to be inappropriate or irrelevant, the workflow can choose to discard it or correct it before proceeding. Anthropic’s description of prompt chaining explicitly mentions adding a *“gate”* – an intermediate check – to verify the process is on track. This could be as simple as checking that an outline contains certain sections, or using a moderation model to filter the LLM’s output at that step. Because the flow is predetermined, inserting such checks does not disrupt the logic; it’s part of the design. Moreover, the workflow can be designed to fail safe – e.g., if a check fails, maybe return an error to the user or fall back to a simpler approach. The point is that **control over the LLM is externalized to the code**. The LLM is not truly acting on its own; it’s a component whose inputs/outputs are fully managed by the surrounding program. This external control also makes **compliance and safety** easier to manage in workflows – you can enforce that certain steps always happen (like a final approval by a human or a content filter before output). Observability also extends to metrics: since each stage is known, you can measure things like “what percent of inputs took branch A vs B” or “how often did we have to retry step 2”, etc., giving clear insight into system behavior.

For **autonomous agents**, observability and control are more challenging because the process is driven internally by the LLM’s decisions. By default, if you just let an agent run, you might only see the final output it produces (and perhaps the tool calls it made if you log those). The reasoning the agent used is inside the model’s head (or hidden in the prompt dialogue) unless you explicitly expose it. A critical best practice is to have the agent **expose its chain-of-thought or plan for each step** – either by printing it to a log or including it in a special section of the model’s output. Anthropic’s second core principle for agents is to *“prioritize transparency by explicitly showing the agent’s planning steps.”*. In implementation, this could mean the agent’s prompt instructs it to output a thought process (often filtered out before giving the final answer to the user, but logged for developers). Some agent frameworks have the concept of a “verbose mode” where the agent will reveal what it’s doing (e.g. “Searching for X -> found Y -> going to do Z next”). By capturing this, developers can observe how the agent is arriving at decisions, which is invaluable for debugging and auditing. It helps answer: *why did the agent take this action?* or *what led to this incorrect answer?*. Without this, the agent can seem like a black box that magically went through some sequence of states.

Control in agents is exercised more indirectly. One method is **setting constraints**: for example, limit the tools the agent can use (prevent it from doing anything outside a certain scope), or limit the number of steps (to prevent runaway loops). Anthropic mentions using *“stopping conditions (such as a maximum number of iterations) to maintain control.”*. Another method is gating certain actions behind user approval – e.g., an agent might be allowed to draft an email but not send it without a human reviewing, or in the context of code, an agent could propose code changes but a human must approve them before execution. Agents can also be designed to defer to humans when unsure: the prompt can encourage the agent to ask the user for guidance if it’s stuck. All these are ways to inject control into an otherwise autonomous process.

**Human oversight** is a special form of observability/control for agents. Because agents may operate for many steps, one strategy is to **have the agent pause at intermediate milestones and request feedback**. For example, after an agent has formulated a plan, it might present the plan to the user: “Here is what I intend to do, shall I proceed?” This way the user or developer can catch a misinterpretation early. Another checkpoint could be after the agent has gathered information and is about to take an action with consequences (like executing code or performing a transaction) – one might require a human review at that point. In a workflow, such human-in-the-loop points could also exist, but they are easier to enforce (just call a human approval step in the code). In an agent, you have to program the agent’s prompt to know when to ask for help or design the agent loop to yield control periodically. It’s more complex, but essential in high-stakes applications.

From a **reliability engineering** perspective, monitoring an agent often involves capturing its entire sequence of actions and results. Logs might include: at time t, agent chose Tool X with input Y, got result Z. Over many runs, this log can be analyzed to see patterns or common failure points. If an agent starts going off-track, detecting that automatically is non-trivial – you might need another model or heuristic to watch the agent’s outputs for signs of confusion (some developers use a secondary “watcher” model to analyze the agent’s thoughts). With workflows, such off-track detection is usually built-in via the gates or expected outputs at each step.

Finally, **guardrails** for agents often have to be learned or adaptive. Anthropic recommends using sandboxed environments and extensive testing to refine these guardrails. For example, in a sandbox you might intentionally feed the agent tricky inputs to see if it does something undesirable, then adjust your prompt or add a constraint to handle that. You may also use automated evaluations or adversarial testing. In contrast, guardrails in workflows are more static (like an if-condition). Both approaches benefit from guardrails; it’s just that in workflows they’re part of the code, and in agents they might be part of the prompt or external monitoring systems.

In conclusion, ensuring **observability** (insight into what the AI is doing) and **control** (ability to direct or correct the AI) is more straightforward in workflows due to their explicit nature. Agents require more careful design to achieve transparency (like logging thoughts) and control (like setting limits and involving humans). Nonetheless, with proper design, agents can be made *sufficiently observable* and safe for practical use. The key is to not treat the agent as an infallible black box but to instrument it with the right reporting and intervention hooks. As a rule of thumb, if you find it hard to explain *why* an AI system made a certain decision, you likely need to add more observability (e.g. have the agent explain itself) or reduce its autonomy.