The uncomfortable truth is this: most “slow agents” aren’t slow because of the model. They’re slow because we built them like polite synchronous APIs in a world that now behaves more like a distributed operating system. Latency shows up before the first token is generated, long before users complain about response quality, and well before observability dashboards light up. By the time you notice it, the damage is already baked into your architecture. Agent-based systems expose this ruthlessly, because they turn small inefficiencies into user-visible pauses. Once you’ve seen that pattern in production, you stop blaming inference and start interrogating everything else.

Agent-based systems performance is not about shaving milliseconds off a single call. It’s about understanding how cold starts, orchestration delays, and response delivery compound across an entire workflow. If you miss that, no amount of model tuning will save you.

How cold starts affect AI agents in production

Cold starts were annoying but tolerable when we were deploying stateless APIs with predictable traffic. In agent-based systems, they’re existential. An agent is rarely just one process waiting on one request. It’s an orchestrator that may spin up multiple workers, load tools, establish outbound connections, and hydrate context before doing anything useful. When that whole chain is cold, latency explodes non-linearly.

Serverless cold starts are the obvious culprit, but they’re only the first layer. Yes, functions waking up from zero add seconds. Less obvious is inference warm-up, especially when you’re using GPU-backed endpoints that aggressively scale down. Model weights need to be paged in, kernels recompiled, and caches repopulated. That delay often dwarfs the actual inference time, yet teams keep benchmarking warm models and wondering why production feels sluggish.

Then there’s tool initialization. Database pools that lazily connect, vector indexes that mmap on first query, SDKs that perform auth handshakes at import time. Each one is small. Together, they form a cold-start tax that hits every new agent execution path. In single-turn chat, users forgive it. In agentic workflows that branch or retry, they don’t.

The real damage shows up when cold starts intersect with orchestration. If your agent framework spins up separate workers per tool or per reasoning step, you can trigger multiple cold starts for a single user request. I’ve seen systems where a “simple” agent response incurred four separate serverless wake-ups before the model even saw the prompt. That’s not an optimization problem. That’s an architectural one.

This is why experienced teams pre-warm aggressively, pin minimum instances, and treat cold paths as production bugs. They also design agents to reuse execution contexts whenever possible, even if that slightly complicates isolation. Performance beats purity when users are waiting.

There’s a deeper discussion on this mindset shift in AI Agents: A Practical Guide for Building, Deploying, and Scaling Agentic Systems , particularly around treating agents as long-lived systems rather than ephemeral calls. Once you internalize that, cold starts stop being an afterthought and become a first-class design constraint.

Reducing latency in multi-step agent workflows

Latency in agent-based systems is rarely additive. It’s multiplicative. Each reasoning step doesn’t just add its own delay; it introduces opportunities for queueing, backpressure, and coordination overhead. By the third or fourth step, users feel like the system is “thinking,” even when most of that time is spent waiting on infrastructure.

Tool invocation latency is the silent killer here. Every time an agent calls out to a database, API, or retrieval system, you pay serialization costs, network hops, and often authentication overhead. Multiply that by retries and fallback logic, and you’re suddenly spending more time outside the model than inside it. Teams often respond by caching aggressively, which helps, but caching without understanding execution order can create its own stalls.

Async execution is the obvious lever, but it’s also frequently misused. Making everything async doesn’t help if you still await results sequentially. High-performance agent systems parallelize speculative steps, cancel aggressively, and collapse branches early. That requires embracing an event-driven agent model rather than a loop-based one. The agent reacts to completed events instead of marching step by step through a script.

This is where agent orchestration decisions matter more than model choice. Frameworks that encourage linear chains feel comfortable but hide latency until it’s too late. Systems that expose execution graphs force you to confront dependencies explicitly. Once you do, you start asking uncomfortable but necessary questions. Does this tool call really need to block reasoning? Can we stream partial context into the model while waiting on slow data? Can we downgrade accuracy slightly to preserve responsiveness?

Horizontal scaling helps, but only if your workflows are designed to benefit from it. Throwing more workers at a tightly coupled agent just increases contention. The teams that get this right treat agent steps as loosely coupled services with clear contracts, not as monolithic chains. There’s a strong overlap here with ideas from Horizontal Scaling Strategies for AI Agent Backends, especially around isolating hot paths from exploratory or fallback logic.

One pattern I keep seeing succeed is collapsing “thinking” and “doing” phases. Instead of pausing the model while tools execute, you stream intermediate reasoning back into a follow-up prompt once results arrive. It feels messy. It is messy. It’s also dramatically faster from the user’s perspective.

Streaming responses in LLM-powered agents

If cold starts and orchestration determine when your agent begins to respond, streaming determines how alive it feels once it does. Streaming AI responses isn’t a UX flourish. It’s a performance strategy. Users perceive systems that stream as faster, even when total completion time is identical. In agent-based systems, streaming can do more than soothe impatience. It can reshape how the entire workflow executes.

Token streaming changes the feedback loop between agent and user. Instead of waiting for a perfect answer, you deliver incremental progress. That opens the door to early interruption, clarification, and even dynamic tool selection. I’ve watched teams reduce perceived latency by half simply by emitting the first token as soon as the model has enough context, even if downstream tools are still running.

Websocket streaming is the workhorse here, but it’s often implemented naïvely. Backpressure handling matters. If the client can’t keep up, buffers grow, memory spikes, and suddenly your “fast” system falls over under load. Streaming needs to be treated as a flow-control problem, not just a transport choice.

Streaming also forces hard decisions about determinism. Once tokens are emitted, you can’t easily roll them back. That means your agent needs higher confidence earlier in the reasoning process. In practice, this pushes teams to separate exploratory reasoning from user-facing output. The agent can think messily in private, then stream only what it’s confident enough to commit to. That separation is subtle but critical.

There’s also an economic angle. Streaming lets you cut off long-running generations when the user disengages, which directly reduces token burn. This connects naturally with strategies discussed in Reducing Token Costs in Long-Running Agent Workflows . Performance and cost stop being separate conversations once you stream everything.

The most advanced systems go a step further and stream tool results alongside model output. Partial retrieval results, progress updates, even structured events. At that point, your agent isn’t just answering questions. It’s narrating its execution. Users trust that far more than a spinning loader.

Now, a brief digression, because this always comes up. Someone will argue that streaming complicates testing, observability, and compliance. They’re right. It does. But so did async APIs, microservices, and distributed tracing when we first adopted them. We accepted that complexity because the alternative was stagnation. The same applies here. If your agent system is mission-critical, you will eventually need to reason about in-flight responses. Avoiding streaming to keep things “simple” is a short-term comfort that turns into long-term pain.

The way back from that complexity is discipline, not avoidance. Strong schemas for streamed events, explicit lifecycle states, and ruthless timeouts. Once those are in place, streaming becomes predictable again, just more expressive.

Bringing this back to the core argument, performance in agent-based systems is holistic. Cold starts determine whether your agent feels responsive at all. Latency across multi-step workflows determines whether it feels competent. Streaming determines whether it feels alive. You can’t optimize these in isolation. They reinforce or undermine each other.

Teams that succeed here don’t chase micro-optimizations. They adopt a posture of performance skepticism. Every new tool, every new agent capability, is assumed guilty until proven fast enough. They measure first-token latency, not just total time. They trace orchestration paths, not just model calls. They accept that some elegance must be sacrificed for speed.

If that sounds exhausting, it is. But it’s also where agent-based systems stop being demos and start being products. Once you’ve lived through that transition, you stop romanticizing “smart agents” and start respecting fast ones.

Sometimes progress comes faster with another brain in the room. If that helps, let’s talk — free consultation at Agents Arcade .