What are agentic workflows?

You've probably built something like this before: a pipeline that pulls data from an API, transforms it, and writes it to a database. It works great until the API changes its response format, or a field comes back null, or the database schema drifts. Now you're patching hardcoded logic at 2 AM. An agentic workflow handles that differently. Instead of following a fixed script, the agent figures out what needs to happen, where to get the data, and how to recover when something breaks mid-run.

That's a meaningful shift. You're moving from rigid, step-by-step automation toward goal-directed systems that observe, plan, act, and adjust in a loop. And it has real implications for how you build, deploy, and run the infrastructure underneath.

In this article, we'll cover how agentic workflows differ from traditional automation, the core design patterns that make them work, and what it takes to run them reliably in production.

Agentic workflows vs. traditional automation

Traditional automation like Robotic Process Automation (RPA) runs on rule-based scripts. If a button moves on a web page, the bot breaks. If a process changes, someone rewrites the rules. It's deterministic and often brittle—when something unexpected happens, a human typically has to step in.

Agentic workflows flip this model. Instead of executing predefined steps, an agent receives a goal and figures out how to accomplish it. The system reasons about what to do next, takes action, observes the result, and adjusts—all in a loop.

Four things tend to set agentic workflows apart:

1. Autonomy: Agents operate independently within defined boundaries. Frameworks like CrewAI define autonomy through parameters like allow_delegation and max_iter, letting agents delegate tasks and control iterations so you can dial autonomy up or down.
2. Reasoning: Agents don't just pattern-match—they perform reasoning and acting. This goes beyond simple input-output mappings to include planning, reflection, and decision-making.
3. Tool use: Agents can call external tools, APIs, and databases to gather information or take actions in the real world.
4. Memory: Agents maintain context across interactions, so they can build on previous work and learn from experience.

These capabilities work together. An agent that reasons well but can't remember past actions or call external tools is still pretty limited.

Core components & control loops

An agentic workflow usually looks less like "a pipeline" and more like a feedback loop. You're wiring together a few building blocks, then letting the system iterate until it hits a stopping condition.

Goal & constraints

Every agent needs a target and guardrails. The goal is what you want ("draft a support response"), and the constraints define what's allowed ("don't access customer Personally Identifiable Information (PII)," "stay under $0.20 per task," "use only these tools"). Without constraints, an "autonomous" agent is basically a distributed incident waiting to happen.

Planner & policy

Some systems have an explicit planner; others let the LLM "plan" implicitly by generating a next action. Either way, you typically end up with:

• A plan representation: tasks, steps, tool calls, or a graph.
• A policy for what happens next: continue, ask a question, retry, escalate, or stop.

How you structure the plan and policy often shapes how predictable (and debuggable) the whole workflow ends up being.

Tooling layer

Tools are how agents touch reality: search, internal docs, ticketing systems, databases, code execution, and deployment APIs. Two things worth watching for:

• Tool outputs are often messy (HTML, partial JSON, weird error strings). Agents need to handle that gracefully.
• Tool calls are where you tend to rack up latency, cost, and security risk.

Getting the tooling layer right often matters more than getting the model right. Flaky tools can undermine even the best reasoning.

Evaluator & stopping conditions

If you've ever watched an agent loop forever because it's "almost done," you've met the need for stopping rules. Evaluators can be simple (max iterations, max cost, timeouts) or smarter (validate output format, sanity check numbers, verify citations, run unit tests). The key is that the agent typically shouldn't be the only thing that decides it's finished.

Memory in agentic systems

Memory is what turns one-off chat into something that feels like a system.

• Short-term memory manages the current conversation context.
• Long-term memory stores information across sessions, allowing agents to recall past interactions and learned preferences.

Optimizing retrieval efficiency is one of the biggest challenges in AI memory design, as storing excessive data can lead to slower response times. If memory isn't persisted properly, agents may have to re-derive information they've already seen, which slows everything down and often raises inference costs.

What "memory" usually means in practice

In real systems, agent memory tends to split into three buckets:

• Conversation state: the running transcript, current plan, intermediate results.
• User/org knowledge: preferences, policies, past actions, prior tickets.
• Working set data: tool outputs you want to reuse (retrieval results, API responses, computed values).

The catch: LLM context windows aren't a memory system. They're a buffer. If you want an agent to remember anything across turns, you need to store it somewhere, retrieve it fast, and keep it fresh.

Why this points to in-memory infrastructure

All of this (short-term state, long-term retrieval, working set data) adds up to a lot of small reads and writes happening while the agent thinks. That's where infrastructure choices start to matter.

Redis is a real-time data platform with a memory-first architecture, delivering sub-millisecond latency for many core operations. For agentic systems, that matters because agents often do many reads per task: pulling conversation state, fetching retrieved context, checking rate limits, writing tool results, and updating progress. Redis also combines vector search with semantic caching in a single platform, so you can handle long-term memory retrieval and reduce repeated LLM inference costs without managing separate systems for each.

What patterns do agentic workflows follow?

Most production agents end up converging on a handful of patterns. Not because everyone copies the same blog post, but because these patterns map nicely to the failure modes you see in the field: runaway loops, slow tool chains, inconsistent outputs, and state that goes missing at the worst time.

Plan-act-observe loop

This is the default.

1. Plan: decide the next step (or update a plan)
2. Act: call a tool or generate an artifact
3. Observe: inspect the result
4. Repeat: continue until done or stopped

It's simple, but it's also where you discover that "a single request" can mean 8–30 internal steps. That's why infra choices start to matter quickly.

ReAct-style tool use

A common structure is "think, then act." The agent reasons briefly, then chooses a tool call. You'll often see this used when tool calls are expensive or risky.

The practical upside: you can log the reasoning (or a summary) and the action separately, which helps when you're debugging why the agent decided to do something questionable at 2 AM.

Decompose & delegate

In multi-agent setups, one agent breaks a goal into sub-tasks and hands them to specialists who work in parallel. This helps when tasks are naturally parallel, tools require different permissions, or you want isolation so one agent can't blow up the whole task. The trade-off is coordination overhead. If your messaging and state storage are slow, you'll feel it.

Retrieval augmented generation (RAG)

Retrieval augmented generation (RAG) improves LLM responses by retrieving relevant context from your own data before generating an answer. In agentic workflows, RAG shows up constantly: "find the customer's last 3 incidents, then draft a response" or "look up the latest runbook, then propose remediation steps." It's not only about accuracy. It's also about keeping prompts smaller by fetching just-in-time context instead of stuffing everything into the conversation.

Memory writeback

Agents that only read context tend to repeat mistakes. Memory writeback is the pattern where the agent saves what it learned: new user preferences, resolved incident summaries, validated answers, tool quirks. Writeback is where you need policies. You don't want an agent writing every random guess into long-term memory.

Guardrails & gates

Guardrails are the checks around an agent, not the agent itself: schema validation, safety checks, cost and time budgets, and approval steps for high-risk actions. This is also a nice place to insert deterministic logic. LLMs are great at messy interpretation, but you still want boring code deciding whether a refund is over the limit.

Human-in-the-loop

A lot of real workflows are "agent does 80%, human does the last 20%." That's not failure; it's often the fastest path to production. You'll typically see this when stakes are high, you're still building trust, or you need a paper trail of approvals.

Where are agentic workflows being used today?

The flashy demos get attention, but real adoption tends to start where the work is repetitive, the inputs are messy, and the cost of being a little wrong is manageable.

• Support ops & ticket triage. Agents can summarize tickets, suggest responses, route issues, and pull relevant Knowledge Base (KB) articles. Impact tends to be straightforward to measure: first response times, escalation rates, and consistency.
• Developer productivity & internal platforms. Internal copilot-style agents handle service discovery, runbook retrieval, incident summaries, and Pull Request (PR) review assistance. These tend to work best when the org already has decent docs and logs. Agents can't retrieve what doesn't exist.
• Sales engineering & customer success. Agents draft follow-up emails from call notes, summarize account history, and propose next steps. This usually starts as assistive tooling, then becomes more autonomous as teams get comfortable with approvals and auditing.
• Finance workflows. Agents can help with invoice processing, anomaly detection triage, and explaining variances in plain language. Guardrails matter more here than in most domains, because mistakes tend to be expensive and often regulated.
• Security operations. Security teams use agentic patterns for alert enrichment, extracting indicators of compromise, and drafting incident reports. Most orgs keep the "take action" part gated. The agent gathers, correlates, and proposes, but humans approve anything that changes systems.
• E-commerce & personalization. Agentic workflows show up as "micro-decisions" chained together: identify intent, retrieve similar products, generate copy, and update recommendations. Latency is a UX concern here. If the agent chain adds noticeable delay, users tend to bounce.

The common thread across all of these: teams typically start with assistive use cases where a human reviews the output, then gradually expand autonomy as they build confidence in the system's reliability.

Why infrastructure matters for agentic workflows

Agentic workflows are rarely compute-bound on the model alone. They're often bound on coordination: memory lookups, retrieval, caching, messaging, and orchestration. A traditional API call might hit one database and return. An agent might fetch conversation state, run retrieval, call an internal API, call an LLM, validate the output, write back memory, and enqueue a follow-up job—all for a single task. Even if each step is "pretty fast," the chain isn't.

Agent runs also aren't stateless. They typically need a place to store intermediate results, a place to store and retrieve long-term memory, and a way to coordinate concurrent workers. When many agents share tools and data, your infrastructure needs to handle spikes without turning into a thundering herd. Contention, atomicity, and fan-out start to matter.

Caching plays a different role here too. In agentic systems, caching can directly reduce LLM spend, not just speed up reads. Traditional caching handles tool outputs like API responses and DB query results. Semantic caching goes further, caching based on meaning so "reset my password" and "help me log back in" can reuse the same underlying completion when it's safe. The trade-off: you need a data layer that can store both operational state and vector embeddings, and retrieve them fast enough that caching doesn't add overhead.

Finally, observability. Running agents blind is rough. You'll want to know what the agent tried to do, which tools it called, what it cost, where it spent time, and why it stopped. Without that, debugging turns into "re-run it and hope it fails the same way"—not a great on-call strategy.

Where to go from here

Agentic workflows are "automation with judgment." You give an AI system a goal, access to tools, and enough state to iterate until it's done, or until your guardrails tell it to stop. The patterns across real deployments are consistent: a plan-act-observe loop, retrieval augmented generation (RAG) for context, memory writeback to avoid repeating work, and gates for anything risky. The failure modes are consistent too: latency creep from long tool chains, missing state, and limited observability.

Redis fits naturally here as the coordination layer. It's a fast, in-memory, real-time data platform that can store short-term state, power low-latency retrieval for long-term memory via vector search, and reduce repeated LLM calls with semantic caching—all in one place. When your workflow does many small reads and writes while it thinks, sub-millisecond access is often the difference between an agent that feels interactive and one that feels stuck.

Try Redis free to prototype these patterns with your own workload, or talk to our team about architecture, latency budgets, and mapping your agent use case onto real infrastructure.