Ubiquitous Agent
As agentic AI starts taking shape and maturing into production workloads, one important aspect that stands out is the Network reliability. Agents are becoming omni-present with the ability to run in a very distributed topology. The early adopters of agents are the humans themselves in getting tasks done in their laptops. Humans work from anywhere and the Network is a variable! As agents permeate various walks of life, AI is the brain and network is the nervous system.
Reliable Software
Software development, historically, emphasized resilience through practices that addressed an inherently unpredictable network. Techniques like circuit breakers, graceful fallback mechanisms, bounded API retries with exponential backoff, localized cache and context, and asynchronous I/O were standard. Protection was built for an unpredictable network, and the results were predictable.
Smart Agents
Agents are autonomous, proactive with decision making capabilities. Agentic AI uses the OODA loop (Observe,Orient,Decide,Act) framework. An agentic AI cycles through this loop to continuously take in new data, interpret its context, make a decision, and act, improving its performance over time through learning and adaptation. This approach moves AI from a simple tool to a more proactive and intelligent partner.
Networks for the Smart AI agents present problems that are hard at a very different plane. A lossy network could result in stale data – affecting inferences, misaligned state data – producing worthless(late) output, reasoning drift due to observation sequences. Traditional solutions like exponential back off on network failures do not solve the agent AI problem but rather distort the output. This impacts the fundamental integrity of the AI's autonomous decision-making. AI, being probable by nature, needs a predictable network!
Network Loss Kills Agent Output and Productivity
Agents have complex dependency-based workflows with sequence of external calls – LLM, Tools, RAG etc., A network error can be interpreted as a solvable problem and agents can get into loops where retries may be initiated causing loads on GPU and wasting tokens. Connections that are slow can create stale context which can cause hallucination due to partial or incomplete information. Tasks may start but never complete and create incoherent internal states.
You have the solution in hand already - Cloudbrink High-Performance ZTNA!
Prevention is better than cure!
When the agent starts behaving inconsistently, reducing the number of variables that can cause the instability becomes very important. Lossy networks , as observed across the world, is an important factor to account for in the design. Cloudbrink with its patented algorithms provides a reliable best quality network for Agent AI. As Agent Developers focus on the AI part of the puzzle, Cloudbrink takes care of the Network Quality!
Observability
Agents can be measured with specific KPIs for task completions and anomalies need to be debugged and fixed. Cloudbrink provides rich metrics to identify the bottlenecks helping in remediating the network issues
