Agentic Observability

21.03.25 11:21 AM By Stuart Hirst

The Rise of Agentic AI and the Need for Observability

Current Solutions and Their Shortcomings

Recognising this need, the market has seen an emergence of tools aiming to monitor AI agents. Some are open-source projects born out of the LLM developer community, while others are commercial platforms from startups and incumbents. For example, LangSmith (from LangChain) and Langfuse offer trace logging of LLM calls; tools like Helicone act as proxies to capture prompt/response data; and Arize/Phoenix and WhyLabs are extending traditional ML monitoring into the generative AI realm. This flurry of innovation is great to see – it shows that as an industry we’re taking AI oversight seriously.

However, most of these solutions address only parts of the problem. A lot of developer-focused tools can log each API call or each prompt, but they struggle to present the higher-level picture – especially when an agent has to string together dozens of steps or collaborate with other agents. Imagine a customer support AI agent that, to resolve one query, might consult a product database, draft an answer, double-check via another AI, and then respond. A human debugging that session needs to see the whole sequence with timestamps and decisions. Few tools today provide that out-of-the-box.

Another issue is fragmentation. One tool might tell you how many times the AI called the language model (and how much that’s costing you), while another might evaluate whether the AI’s answer was factually correct. You might resort to custom scripts or manual analysis to connect the dots – hardly a sustainable strategy. This fragmentation means higher overhead to achieve observability, and potential blind spots if some piece isn’t captured.

Crucially, many existing solutions weren’t built with enterprise governance in mind. They were made by and for AI developers, which is fantastic for early-stage experimentation, but enterprises have stricter demands. Logging data may need to be kept private or on-premises. Different teams might want role-based access (e.g., only data scientists can view raw prompts with customer data, whereas business owners just see aggregate metrics). Compliance logging – say, keeping records to comply with the upcoming EU AI Act – might not be considered in a hobby project that focuses just on debugging model prompts.

In short, while we do have tools signalling the start of “AI observability” as a practice, there’s a gap in integrated, enterprise-ready solutions that cover the full lifecycle of agentic AI. Businesses adopting AI agents today often express frustration that they lack a “single source of truth” for understanding their AI’s behavior. They might be cobbling together an internal dashboard or exporting logs to Excel – approaches that won’t scale when they go from one pilot agent to a fleet of fifty automating various processes.

Our Vision: The Central Observatory for All Your AI Agents

Holistic View

Pebble AI doesn’t just show you API calls or chat transcripts in isolation. It reconstructs the full narrative of each agent’s task. If your AI sales assistant goes through 10 steps to generate a lead, you’ll see all 10 in order – along with timing, cost, and outcome at each step. This holistic trace is presented in an easy, visual timeline UI, so you can literally replay an agent’s thought process. Think of it like debugging a complex program – but the “code” is the agent’s decisions and the “debugger” is Pebble AI, stepping through each function call (be it an LLM call or a database query).

Integrated Governance Features

We know observability data isn’t just for engineers – it’s for compliance officers, risk managers, and business leaders. Pebble AI comes with audit trail exports and compliance dashboards out-of-the-box. Need to comply with that EU AI Act logging requirement? Your Pebble console can generate a report of all agent decisions, complete with timestamps and annotations about who/what reviewed them, in a format ready for auditors. We also allow setting policies in the platform – for instance, “if the agent’s confidence is below X, require human review” – and Pebble will help enforce those by catching low-confidence outputs and alerting a human operator. By baking governance in, we remove the need for separate manual processes to supervise the AI; oversight is part of the system’s DNA.

Multi-Agent Aware

Organisations are starting to deploy not just one agent in a silo, but suites of agents that might collaborate. Pebble AI is built to handle that reality. You can monitor not only individual agents, but also the interplay between them. For example, if you have one AI agent summarizing data and another agent taking that summary to draft a report, Pebble will show the handoff. We support tagging and grouping of agents, so you can view metrics for a single agent or aggregate for a team of agents working on a process. This is agentic observability at the system level, not just the component level.

Intelligent Insights, Not Just Data

Raw logs are not enough. Pebble AI is incorporating analytics and AI to help interpret the observability data. Our platform can automatically flag unusual patterns (using anomaly detection models) and even score the quality of agent outputs using built-in evaluators. For example, Pebble might highlight that “This week, Agent A’s average task completion time doubled compared to last week” or “Agent B has produced answers with a much more negative tone than usual – possible issue?”. These insights are surfaced in a dashboard and can be sent as alerts. The idea is to offload the burden of analysis – Pebble doesn’t just collect data, it helps you make sense of it at a glance. Ultimately, this means you spend less time sifting through logs and more time optimizing and strategizing your AI deployments.

Flexibility and Integration

Pebble AI is designed API-first. We know each company might use a different stack – you might have some Python agents using LangChain, some Node.js services calling the OpenAI API directly, maybe an off-the-shelf chatbot platform – and ripping those out is not feasible. So instead, Pebble AI provides connectors and SDKs to plug into any environment. We want to meet you where you are: whether it’s via a simple REST endpoint where you send us logs, or a direct integration with your cloud provider’s AI services, or a drop-in library that instruments calls. Similarly, we make it easy to pull data out: want to push an alert to Slack or your SIEM when an agent makes a critical error? Or query our system from your BI tool to see quarterly trends? All doable. Pebble AI acts as a hub, not a closed box. This stands in contrast to some solutions that lock you into their interface or require all-or-nothing adoption.

Enabling Sustainable AI Automation

When agentic AI is deployed with proper observability and control, it becomes a reliable engine for automation. We move past the fear of “AI behaving badly” because we have the tools to catch and correct any misbehavior. This opens the door for businesses to automate more and innovate faster with AI.

Picture a future (not far off) where every company has dozens of specialized AI agents: researchers, assistants, analysts, customer reps. Managing that could be chaos – unless you have a way to see and manage all agents centrally. Pebble AI aspires to be that command center that makes large-scale AI agent deployment not only possible, but smooth and sustainable.

At Pebble AI, we believe transparency is empowerment. By shedding light on AI agents’ inner workings, we empower businesses to embrace these agents confidently. Instead of treating AI like a mysterious black box or a quirky intern that sometimes “figures things out somehow,” companies can treat AI agents as fully accountable team members – whose work is tracked, quality-checked, and aligned with the company’s goals and values.

The importance of agentic observability cannot be overstated. It’s how we ensure AI continues to serve us, not stray into unintended territory. It’s how we scale one successful pilot into a thousand agents creating value across an enterprise. And it’s how we build trust – with users, with regulators, and with ourselves that we are in control of our creations.

Pebble AI is proud to be at the forefront of this movement. We’re not just building a product; we’re helping shape the practices and standards that will define responsible AI automation in the years to come. If you’re on the journey of adopting AI agents, we invite you to partner with us. Together, we can unlock incredible productivity while staying true to the principles of transparency and accountability.