When 30+ AI agents diagnose your network, can you trust them?
Imagine dozens of AI agents working in unison to troubleshoot a single network incident—10, 20, even more than 30. Every decision matters, and you need full visibility into how these agents collaborate. This is the final installment in our three-part series on Deep Network Troubleshooting.
In the first blog, we introduced the concept of using deep research-style agentic AI to automate advanced network diagnostics. The second blog tackled reliability: we covered reducing large language model (LLM) hallucinations, grounding decisions on knowledge graphs, and building semantic resiliency.
All of that is necessary—but not sufficient. Because in real networks, run by real teams, trust is not granted just because we say the architecture is good. Trust must be earned, demonstrated, and inspected. Especially when we’re talking about an agentic system where large numbers of agents may be involved in diagnosing a single incident.
In this post, you’ll learn:
- How we make every agent action visible and auditable
- Methods for measuring AI performance and cost in real time
- Strategies for building trust through transparency and human control
These are the core observability and transparency capabilities we believe are essential for any serious agentic AI platform for networking.
Why trust is the gatekeeper for AI-powered network operations
Agentic AI represents the next evolution in network automation. Static playbooks, runbooks, and CLI macros can only go so far. Networks are becoming more dynamic, more multivendor, more service-centric troubleshooting must become more reasoning-driven.
But here’s the hard truth: no network operations centers (NOC) or operations team will run agentic AI in production without trust. In the second blog we explained how we maximize the quality of the output through grounding, knowledge graphs, local knowledge bases, better LLMs, ensembles, and semantic resiliency. That’s about doing things right.
This final blog is about showing that things were done right; or, when they weren’t, showing exactly what happened. Because network engineers don’t just want the answer, they want to see:
- Which agent performed which action
- Why they made that decision
- What data they used
- Which tools were invoked
- How long each step took
- How confident the system is in its conclusion
That’s the difference between “AI that gives answers” and AI you can operate with confidence.
Core transparency requirements for network troubleshooting AI
Any serious agentic AI platform for network diagnostics must provide these non-negotiable elements to be trusted by network engineers:
- End-to-end transparency of every agent step
- Full audit trail of LLM calls, tool calls, and retrieved data
- Forensic capability to replay and analyze errors
- Performance and cost telemetry per agent
- Confidence signals for model decisions
- Human-in-the-loop entry points for review, override, or approval
This is exactly what we are designing into Deep Network Troubleshooting.
Radical transparency for every agent
Our first architectural principle is straightforward but non-trivial to implement: everything an agent does must be visible. That concept means that we expose:
- LLM prompts and responses
- Tool invocations (CLI commands, API calls, local knowledge base queries, graph queries, telemetry fetches)
- Data retrieved and passed between agents
- Local decisions (branching, retries, validation checks)
- Agent-to-agent messages in multiagent flows
Why is this so important? Because errors will still happen. Even with all the mechanisms we discussed in this blog series, LLMs can still make mistakes. That’s acceptable only if we can:
- See where it happened.
- Understand why it happened.
- Prevent it from happening again.
Transparency is also important because we need postmortem assessment of the troubleshooting. If the diagnostic path chosen by the agents was suboptimal, ops engineers must be able to conduct a forensic review:
- Which agent misinterpreted the log?
- Which LLM call introduced the wrong assumption?
- Which tool returned incomplete data?
- Was the knowledge graph missing a relationship?
This review lets engineers improve the system over time. Transparency builds trust faster than promises.
When engineers can see the chain of reasoning, they can say: “Yes, that’s exactly what I would have done—now run it automatically next time.”
So, in Deep Network Troubleshooting we treat observability as a first-class citizen, not an afterthought. Every diagnostic session becomes an explainable trace.
Performance and resource monitoring: the operational viability dimension
There’s another, often ignored, dimension of trust: operational viability. An agent may reach the right conclusion, but what if:
- It took 6x longer than expected.
- It made 40 LLM calls for a simple interface-down issue.
- It consumed too many tokens.It triggered too many external tools.
In a system where multiple agents collaborate to resolve a single trouble ticket, these operational elements are significant. Networks run 24/7. Incidents can trigger bursts of agent activity. If we don’t observe agent performance, the system can become expensive, slow, or even unstable.
That’s why a second core capability in Deep Network Troubleshooting is per-agent telemetry, including:
- Time metrics: task completion duration, subtask breakdown
- LLM usage: number of calls, tokens sent and received
- Tool invocations: count and type of external tools used
- Resilience patterns: retries, fallbacks, degraded operation modes
- Behavioral anomalies: unusual patterns requiring investigation
This approach gives us the ability to spot inefficient agents, such as those that repeatedly query the knowledge base. It also helps us detect regressions after updating a prompt or model, enforce policies like limiting the number of LLM calls per incident unless escalated, and optimize orchestration by parallelizing agents that can operate independently.
Trust, in an operations context, is not just “I believe your answer;” it’s also “I believe you will not overload my system while getting that answer.”
Confidence scoring for AI decisions: making uncertainty explicit
Another key pillar in Deep Network Troubleshooting: exposing confidence. LLMs make decisions—pick a root cause, select the most likely faulty device, prioritize a hypothesis. But LLMs typically don’t tell you how sure they are in a way that is useful for operations.
We’re combining multiple methods to measure confidence, including consistency in reasoning paths, alignment between model outputs and external data (like telemetry and knowledge graphs), agreement across model ensembles, and the quality of retrieved context.
Why is this important? Because not all decisions should be treated equally. A high-confidence decision on “interface down” may be auto-remediated without human review. A low-confidence decision on “possible BGP route leak” should be surfaced to a human operator for judgment. A medium-confidence decision may trigger one more validating agent to gather additional evidence before proceeding.
Making confidence explicit allows us to build graduated trust flows. High confidence leads to action. Medium confidence triggers validation. Low confidence escalates to human review. This calibrated approach to uncertainty is how we get to safe autonomy—where the system knows not just what it thinks, but how much it should trust its own conclusions.
Forensic review as a design principle
We said it earlier, but it deserves its own section: we design for the assumption that mistakes will happen. That’s not a weakness—it’s maturity.
In network operations, MTTR and user satisfaction depend not only on fixing today’s incident but also on preventing tomorrow’s recurrence. An agentic AI solution for diagnostics must let you replay a full diagnostic session, showing the exact inputs and context available to each agent at each step. It should highlight where divergence started and, ideally, allow you to patch or improve the prompt, tool, or knowledge base entry that caused the error.
This closes the loop: error → insight → fix → better agent. By treating forensic review as a core design principle rather than an afterthought, we transform mistakes into opportunities for continuous improvement.
How we keep humans in control
We’re still at an early stage of agentic AI for networking. Models are evolving, tool ecosystems are maturing, processes in NOCs and operations teams are changing, and people need time to get comfortable with AI-driven decisions. Deep Network Troubleshooting is designed to work with humans, not around them.
This means showing the full agent trace alongside confidence levels and the data used, while letting humans approve, override, or annotate decisions. Critically, those annotations feed back into the system, creating a virtuous cycle of improvement. Over time, this collaborative approach builds an auditable, transparent troubleshooting assistant that operators actually trust and want to use.
Putting it all together
Let’s connect the dots across the three posts in the series. Blog 1 established that there’s a better way to do network troubleshooting: agentic, deep research–style, and multiagent. Blog 2 explored what makes it accurate, requiring stronger LLMs and tuned models, knowledge graphs for semantic alignment, local knowledge bases for authoritative data, and semantic resiliency with ensembles to handle inevitable model errors.
Blog 3 (this one) focuses on what makes it trustworthy. We need full transparency and audit trails so operators can understand every decision. Performance and cost observability per agent ensures the system remains economically viable. Confidence scoring qualifies decisions, distinguishing between actions that can be automated and those requiring human judgment. And human-in-the-loop controls the adoption pace, allowing teams to gradually increase trust as the system proves itself.
The formula is simple: Accuracy + Transparency = Trust. And Trust → Deployment. Without trust, agentic AI remains a demo. With trust, it becomes day-2 operations reality.
Join the future of AI-powered network operations
We take network troubleshooting seriously—because it directly impacts your MTTR, SLA adherence, and customer experience. That’s why we’re building Cisco Deep Network Troubleshooting with reliability (Blog 2) and transparency (Blog 3) as foundational requirements, not afterthoughts.
Ready to transform your network operations? Learn more about Cisco Crosswork Network Automation.
Want to shape the next generation of AI-powered network operations or test these capabilities in your environment? We’re actively collaborating with forward-thinking network teams; join our Automation Community.
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