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- What Is Agentic AI? Definition, How It Works, and Examples
Agentic AI is artificial intelligence that pursues a goal on its own. Given an objective, it plans the steps, uses tools and APIs to carry them out, checks the results, and adjusts until the job is done, with limited human supervision. Generative AI returns one response per prompt; an agentic system keeps working toward an outcome.
This guide covers the core components of an agentic system, how the agent loop works, how agentic AI differs from generative AI and traditional automation, the main architecture patterns, real-world use cases, how to test agentic systems, and the risks to plan for.
Overview
Agentic AI systems take a goal, plan the steps, act through tools, and check their own results in a repeating loop. They build on generative AI but act instead of only answering, which makes them useful for multi-step work and harder to test, because the same goal can take a different path on every run.
- Agentic AI: Pursues multi-step objectives by perceiving its environment, planning actions, using tools or APIs, and adjusting its behavior based on feedback.
- Generative AI: Produces text, code, or images in response to a single prompt, and is usually the reasoning engine inside an agentic system.
- Four-stage loop: Agents perceive the environment, plan the next steps, act with tools, and reflect on the results, repeating until the goal is met or a stopping condition is hit.
- Non-deterministic evaluation: Testing an agent means scoring its behavior across many scenarios, mocking the tools it calls, and red-teaming it, rather than asserting one fixed output.
- Agentic testing: The TestMu AI agentic testing platform applies agentic AI to quality assurance through KaneAI, which plans coverage, writes natural language tests, runs them on real browsers, and self-heals.
What Is Agentic AI?
Agentic AI is a class of autonomous artificial intelligence systems that can plan, decide, and perform goal-directed actions with minimal human supervision. Given a goal, the system breaks it into sub-tasks and uses tools and memory to execute them. It then adjusts its actions based on the results and loops until the goal is reached.
The word "agentic" comes from "agent", any entity capable of independent action and decision-making. Generative AI stops after one output: ChatGPT answers your question, Midjourney draws your image, and nothing more happens until you prompt again. An agentic system keeps working toward the goal across many steps.
Agentic AI and AI agents are related but not the same thing. An AI agent is the individual program that perceives, decides, and acts. Agentic AI describes the autonomous, goal-seeking behavior that a system of one or more agents shows. Our guide to AI agents covers agent types and components in depth.
What Are the Core Components of an Agentic AI System?
An agentic AI system has four core components: a reasoning engine (usually a large language model), memory, tools, and the agent loop that connects them. Each one is a structural requirement. Without tools the system cannot act, and without the loop it cannot check its own work.
The Brain: LLM as the Reasoning Engine
In most modern agentic systems, the reasoning engine is a large language model (LLM). The LLM is not just for generating text; it interprets the goal and drives the decisions the agent makes. Earlier agents ran on hand-written rules or planners, and some production systems still combine an LLM with those.
The architecture of agentic AI typically includes core components like planning modules, vector or semantic memory for persistence, natural language processing, tool-use interfaces for API interaction, and reinforcement or self-reflective learning engines that adapt over time.
When you give an agent a goal, say, "test the checkout flow on this e-commerce site and flag any failures", the LLM is the component that interprets that goal, decides what sequence of steps makes sense, evaluates the outcome of each step, and recalibrates its approach if something goes wrong. It is the part of the system that understands context, handles ambiguity, and makes judgment calls that a traditional rule-based script simply cannot.
This is why the quality of the underlying model matters so much. A weak LLM will misinterpret goals, get stuck in loops, or take wrong actions. A powerful one with proper access to tools and memory can complete complex, multi-step workflows that previously required human expertise to orchestrate.
The Memory: Short-Term vs Long-Term
One of the things that separates a capable AI agent from a simple chatbot is memory. Without memory, every interaction starts from scratch. With memory, an agent can reason across sessions, learn from past results, and build institutional knowledge over time.
AI agents typically have two kinds of memory working together:
- Short-term memory (also called working memory or context) refers to everything the agent is actively holding in its attention during a single session. This is the full conversation history, the current task state, recent tool outputs, and the agent's internal chain-of-thought reasoning. It is fast and immediately accessible, but it is temporary. When the session ends, it is gone.
- Long-term memory is persistent storage that survives between sessions. Retrieval-Augmented Generation (RAG) enables agents to retrieve relevant past test artifacts, such as previous test results, failure patterns, or test case metadata, from vector databases that serve as AI agents' memory, thereby informing analysis and planning. This is what allows an agent to remember that a particular UI element always causes flakiness, or that a specific API endpoint has been unreliable in the past.
For software testing, long-term memory is transformative. A testing agent that remembers failure patterns from six months of test runs can intelligently prioritize which tests to run first, focus energy on historically unstable areas of the codebase, and avoid re-testing parts of the application that rarely fail.
The Tools: What the Agent Can Actually Do
An LLM by itself can only produce text. What turns that text-generating capability into real-world action is a set of tools and external functions the agent can invoke to interact with systems, retrieve data, and take actions in the environment.
Common tools available to AI agents include web browsers and UI interaction frameworks, API callers, file system readers and writers, database query interfaces, code executors, search engines, and calendar or task management integrations. Agents built for AI app testing go beyond traditional scripted automation.
As self-governing software systems, AI agents make decisions intelligently based on historical data and adapt to changing environments, generate new test cases dynamically based on risk assessment, detect changes in user interfaces or APIs and automatically update test scripts through self-healing test automation, and analyze patterns in historical test data to predict potential defects.
The more tools an agent has access to, the more autonomously it can operate. A well-equipped testing agent might open a browser, navigate to a URL, interact with UI elements, call APIs, log the results, compare them against expected behavior, and write a failure report without a human touching the keyboard at any point.
The Agent Loop: Perceive, Plan, Act, and Reflect
The cycle that ties all of this together is the agent loop: perceive, plan, act, and reflect. Some sources shorten it to perception, reasoning, and action (PRA), folding reflection into reasoning. It is the core operating cycle of any agentic system, and it repeats until the goal is met or the agent decides it can't proceed.
- Perceive is how the agent takes in information about its current state. This might mean reading test output logs, inspecting a web page's DOM, receiving the result of an API call, or processing a screenshot. The agent normalizes this raw input into a form it can reason about.
- Plan is where the LLM earns its place. Given what the agent has perceived, it interprets what's happening, forms a hypothesis about what to do next, and selects an action from its available tools. This may involve weighing several possible next steps and choosing the one most likely to advance the goal.
- Act is the execution: the agent calls a tool, modifies state, sends a request, clicks a button, or does whatever the planning step decided.
- Reflect is the check. The agent compares the result of the action with what it expected, decides whether the step worked, and revises the plan if it didn't. The outcome feeds into the next perception cycle.
This loop can run hundreds of times during a single agent task. A testing agent working through a complex user journey might perceive the current page state, reason that it needs to click the "Add to Cart" button, take that action, then perceive the new page state, reason that the cart total isn't updating correctly, and flag that as a potential bug, all without human instruction at any of those steps.
This loop is what separates agents from traditional scripts.
How Agentic AI Differs From Other AI Approaches
The table sets agentic AI beside traditional automation and generative AI. For a full breakdown of the second pair, with examples, risks, and how to test each, see agentic AI vs generative AI.
| Dimension | Traditional Automation | Generative AI | Agentic AI |
|---|---|---|---|
| Trigger | Rule or schedule | User Prompt | Goal Objective |
| Memory | None | Session only | Short and long-term |
| Decision-making | Pre-programmed | None | Autonomous, in-loop |
| Handles ambiguity | No | Partially (generates a plausible response) | Yes |
| Adapts to change | No | No | Yes |
| Best for | Stable, repetitive processes | Content, drafting, Q&A | Multi-step workflows requiring judgment |
How Does Agentic AI Work?
Every agentic system, whatever it is built on or for, takes a task through the same five steps, with the agent loop above running inside steps 2 to 5:
- Receive a goal: The agent is given an objective, not a script. "Find all failing tests in the last deployment and generate a fix proposal for each one". This is a goal. "Click the login button" is a script. The distinction matters because goal-oriented instructions leave the agent room to reason about the approach.
- Decompose the goal into sub-tasks: The reasoning layer, usually an LLM, breaks the goal into a sequence of steps. This decomposition is not fixed in advance; it depends on what the agent knows about the environment and what it discovers as it works.
- Select and execute a tool: Each step, the agent identifies the right tool from its available set and calls it. In testing, that might mean calling the browser tool to open a page, the DOM parser to read element state, or the API client to send a request. The tool returns a result.
- Observe and reason: The agent reads the tool's output and decides what to do next. Did the page load correctly? Did the API return the expected schema? Does the result change the plan? If a step fails, the agent does not simply stop; it assesses whether to retry, adjust, or escalate.
- Loop until the goal is achieved or a stopping condition is met. The agent keeps cycling through steps until the goal is complete, it runs out of allowed attempts, or it determines the goal cannot be achieved and surfaces that finding to the human.
Failure Modes in Agentic AI: What Happens When the Loop Breaks
Understanding failure modes is as important as understanding the happy path.
- The most common failure is the agent getting stuck in a reasoning loop, generating a plan, executing it, finding it did not work, generating a slightly different plan, executing that, and repeating indefinitely. This is why production agentic systems always require explicit maximum-iteration limits and graceful exit conditions. Without them, an agent that cannot complete a goal will keep burning API tokens until something external stops it.
- A second common failure is the agent acting on stale information. Agentic systems work from the context window, the information they have been given or have gathered during the session. If the environment changes mid-session (a deployment happens, a feature flag flips, a database is updated), the agent may continue making decisions based on an outdated view of the world.
- A third failure is incorrect tool selection: the agent calls the right kind of tool but with wrong parameters, or calls the wrong tool entirely for a step. LLMs can confidently call tools with incorrect parameters, which is why every production tool integration needs explicit input validation and structured error messages that the agent can reason about. Silent failures, where a tool returns null and the agent does not notice, are a particularly common trap.
Note: See agentic AI in action for QA: KaneAI plans, writes, and runs tests in plain English across 3,000+ browsers and 50,000+ real devices on TestMu AI. Try TestMu AI free.
Common Agentic AI Architecture Patterns
Different tasks require different agent architectures, and choosing the wrong one is a common source of production failures. Common Agentic AI architecture patterns include:
- ReAct (Reasoning + Acting): ReAct is the foundational pattern and the right default for general-purpose agents. It was introduced in 2022 by Yao et al., whose paper has a language model generate reasoning traces and task-specific actions in an interleaved manner. The agent alternates between a reasoning step (thinking about what to do next, given what it observed) and an acting step (actually doing it). Each observation from the action step feeds into the next reasoning step, creating a tight feedback loop. ReAct is the most battle-tested of all agent patterns in production, and it maps naturally to how humans approach unfamiliar tasks: think, try, observe, think again. For testing agents, the ReAct loop handles the core execution cycle; the agent reasons about what to test, executes a step, observes the result, reasons about whether it passed or failed, and proceeds accordingly.
- Reflection: Reflection adds a self-critique step after the agent generates an output. Instead of accepting its first result, the agent enters critic mode, evaluates its own work against explicit criteria, identifies problems, and produces a revised version. This cycle repeats until quality thresholds are met.
- Plan-and-Execute: Plan-and-Execute separates the planning phase from the execution phase. A planning agent produces a complete, structured plan before any execution begins. A separate execution agent then works through the plan step by step. If a step fails, the planner is re-engaged to revise the remaining plan based on the new information.
- Multi-Agent Orchestration: Multi-Agent architectures assign specialized agents to specific roles and coordinate them through either a supervisor (hierarchical) or a peer network (decentralized). The architecture pattern at the orchestration level is the Orchestrator-Worker: a coordinator agent manages sub-tasks and delegates to specialist workers, collecting and synthesizing their outputs. In an AWS benchmark of enterprise scenarios, multi-agent collaboration raised goal success rates by up to 70% over single-agent setups (Shu et al., 2024).
- Human-in-the-Loop (HITL): Certain decisions should always route to a human before the agent can proceed. It is the right design choice for any action that is irreversible, high-stakes, or outside the agent's demonstrated reliability envelope. In testing, HITL means that agent-generated tests are reviewed before they are merged, that agent-proposed changes to existing tests are flagged for approval, and that agent-identified bugs go through human triage before becoming tickets. Any production agentic system touching real customers or real money that does not implement HITL is taking on a big risk.
Agentic AI in Software Testing
Software testing has always been one of the most difficult parts of software development. Writing test scripts, maintaining them as the codebase changes, analyzing failures, and reporting results consume enormous amounts of engineering time.
Agentic AI is changing that completely. Software testing is undergoing its biggest transformation since the shift from manual to automated testing. The driver this time is not just automation, it is agentic AI testing, run by systems that can reason, plan, and act autonomously across complex testing workflows.
From Script-Based to Goal-Based Testing
Traditional automated testing is script-based at its foundation. A QA engineer writes a Selenium or Playwright script that says: open this URL, find this element by this selector, click it, and assert that this text appears. The script is brittle. If the selector changes because a developer renamed a CSS class, the test breaks. If the page layout shifts during an A/B test, the test breaks. If a new modal appears during checkout, the test breaks.
Engineers spend a large share of their time maintaining these brittle scripts. Test automation vendor CloudQA estimates that script maintenance can consume nearly 50% of a QA team's effort in traditional setups.
Goal-based testing, also called agentic testing, changes this at the foundational level. Rather than telling the system every step, you tell it the goal: "Verify that a new user can register, add a product to their cart, and complete a purchase." The agent figures out the steps. Agentic QA uses autonomous AI agents to plan, execute, and maintain software tests based on goals rather than scripts.
The Five Things Agents Change for Your QA Team
Here are the five things agentic AI changes for QA teams:
- Test Generation: Instead of manually writing test cases, agents generate them automatically from requirements, user stories, or even by simply observing the application, and this dramatically speeds up the test creation process.
- Self-Healing Tests: One of the biggest costs in test automation is maintenance. Agentic systems can dynamically re-identify elements and update test logic in response to application changes, drastically reducing the time you spend fixing broken tests.
- Intelligent Bug Detection: An agent doesn't just report that a test failed; it can analyze logs, trace execution paths, perform root cause analysis and even suggest a fix.
- Exploratory Testing: Agents can act as explorers, constantly probing the application to uncover edge cases, vulnerabilities, and unexpected behaviors that scripted tests would miss.
- Shift in Human Role: Agentic AI doesn't eliminate the need for QA engineers. Instead, it frees them from the difficult tasks of writing and maintaining scripts. The QA role evolves into an AI supervisor, someone who sets the goals, reviews the agent's work, and uses their domain expertise to guide the agent's strategy.
Real-World Use Cases For Agentic AI
Agentic AI is used in software testing, software development, healthcare, finance, customer operations, and cybersecurity. Where a bullet names a deployment, the details come from that vendor's own published account.
- Software Testing and QA: This is the most popular use case, and one of the most mature deployments of agentic AI in production. Testing is exactly the kind of multi-step, context-dependent workflow where agents excel at understanding requirements, generating test cases, executing them, analyzing failures, and updating the suite as the application changes. That workflow, split across dedicated agents, is the focus of our AI test agents page.
- Software Development, Coding Agents: A coding agent takes an issue or a task description and works through it without step-by-step prompts. GitHub documents its Copilot cloud agent as able to research a repository, plan changes, and implement them in the background, editing files and running tests and linters in an ephemeral environment. A developer can review the changes and request refinements before a pull request is created.
- Healthcare, Clinical and Administrative Workflows: On the administrative side, agents update electronic health records from lab systems, wearables, and telehealth visits. They optimize scheduling, predict bed occupancy, and manage staffing based on dynamic demand. On the clinical side, agents analyze data from monitoring tools and scans, flag early signs of deterioration, and surface diagnostic suggestions to clinicians. The agent never makes the clinical call, the physician does, but it sharply cuts the time needed to gather and synthesize information.
- Finance, Fraud Detection and Reconciliation: Agents monitor transactions continuously, cross-referencing known fraud indicators and flagging anomalies in real time. Unlike rules-based detection, which misses novel patterns, agentic systems reason about context, whether a transaction is unusual for this account's history. They weigh amount, location, and timing together to judge whether the combination matches a specific known attack pattern, rather than checking each signal in isolation.
- E-commerce, Customer Operations at Scale: Resolving support queries, handling returns, and managing escalations is among the highest-volume, most repetitive work in e-commerce and also highly variable, since every customer situation differs slightly. Agentic systems handle full resolution for straightforward cases: reading the issue, checking order status, verifying eligibility, processing the return, and confirming it. Complex cases escalate to humans, who receive a case already investigated and summarized rather than one to start from scratch. Klarna reported in February 2024 that its AI assistant handled 2.3 million conversations in its first month, two-thirds of its customer service chats, covering refunds, returns, cancellations, and disputes.
- Cybersecurity, Threat Detection and Response: Security operations centers deal with enormous alert volumes, most of which are false positives. Analysts spend most of their time triaging rather than investigating genuine threats. Agentic systems handle initial triage, reading alert data, cross-referencing threat intelligence, and correlating network activity to judge severity. Low-confidence alerts are dismissed with reasoning logged; high-confidence threats trigger automated containment while the analyst is notified with a full summary. Agents also hunt for flaws before attackers do: Google's Big Sleep agent, built by Project Zero and Google DeepMind, found an exploitable stack buffer underflow in SQLite in 2024, and it was fixed before it reached an official release.
For a wider catalog across industries, see our roundup of agentic AI examples. For specific products, from coding and research agents to security and healthcare agents, the list of AI agent examples names the maker and what each one does.
How Do You Test and Evaluate Agentic AI Systems?
Agentic pipelines are non-deterministic. Two identical prompts can lead to different reasoning paths, tool choices, or retrieval strategies. Success is not only about the final answer, but it is also about how the agent actually got there.
Key Metrics for Evaluating Agentic AI Systems
- Task Completion Rate: Did the agent complete the assigned task? For a testing agent, did it produce a runnable test? For a support agent, did it resolve the ticket without escalating? Completion rate is necessary but not sufficient; an agent can complete a task badly.
- Trajectory Accuracy: For a given goal, what is the optimal path through the available tools and reasoning steps? Trajectory accuracy measures how closely the agent's actual execution matches that of the optimal path. An agent with a high completion rate but low trajectory accuracy is getting to the right answer the long way, burning more tokens and time than necessary.
- Tool Call Accuracy: Measures whether the agent calls the right tools with the correct parameters. A customer service agent can achieve high task completion while still calling the wrong API endpoint on a subset of cases, and those cases surface as customer complaints rather than test failures. The guide to agent performance covers tool correctness and trajectory efficiency alongside the benchmarks used to compare agents.
- Goal Fulfillment: It measures whether the agent actually achieved what the human needed, as defined by a human reviewer rather than an automated metric. A research agent can call every required API and still deliver a summary that a domain expert would reject. Goal fulfillment captures that gap.
- Cost Per Run: Tracks token consumption and execution time per agent run. For agents running at scale, cost per run is a direct line item. Teams that optimize for goal fulfillment without tracking costs often discover that their agents are effective but economically unsustainable at the scale required to run.
Which tooling fits depends on what the agent does. For conversational agents that talk to customers over chat, voice, or phone, TestMu AI Agent Testing scores behavior across scenarios with synthetic users instead of asserting a single output. For agents that act, calling tools, writing files, or hitting APIs, Agent Assurance (Rook CLI) checks what the agent actually did against the task; it is self-serve, with a 14-day free trial. The wider practice is covered in our guide to AI agent evaluation.
In the TestMu Conference 2026 session Agentic AI Changed What We Test, And How We Test It, Pooja Oza works through what breaks when the same prompt returns a different, and not necessarily wrong, output on every run, and an agent picks a different path to the same goal.
How to Run an Agentic Test in Plain English With KaneAI
The fastest way to see the agent loop at work is to give a testing agent a goal and watch it plan. KaneAI, TestMu AI's GenAI-native testing agent, works this way: you describe the test in natural language, and it plans the steps, runs them on a real browser, and reports what happened.
- Describe the goal. For example: "Go to the eCommerce Playground login page, sign in with my test account, and verify that the account dashboard shows the correct name." The store used here is the eCommerce Playground demo store; register your own test account there first.
- Review the plan. The agent breaks the goal into discrete steps (open the page, fill each field, submit, check the URL and the welcome text) and shows them before it runs, so you can adjust them.
- Run and inspect the evidence. The test executes on a cloud browser and returns a pass or fail with screenshots, a video, and network and console logs.
- Iterate or export. Change the test by describing the change. Passing tests can be exported to Selenium, Playwright, Cypress, or Appium, or run in parallel on HyperExecute, TestMu AI's test orchestration cloud.
The getting started with KaneAI documentation covers setup in more detail.
Risks and Limitations of Agentic AI
Agentic AI carries real risks, and plenty of projects stall on them. Gartner predicts that over 40% of agentic AI projects will be canceled by the end of 2027 because of escalating costs, unclear business value, or inadequate risk controls. For agents that act on other systems, the list of agentic AI risks pairs each risk with a test to run before release. The limitations below matter most when agents are used for testing.
Hallucination in Testing Means False Passes
LLMs can hallucinate, generating output that sounds confident but is factually wrong. In testing, this produces false passes, a test reporting success when the application is actually broken. A false pass is worse than a broken script, since a broken script at least signals red.
Mitigate this by capturing screenshots, logs, and evidence for every assertion, and treating agent results as inputs to human review, not final verdicts.
Cost Per Agent Run Adds Up at Scale
Every agent invocation consumes LLM tokens, browser cloud resources, and compute time. Negligible for small suites, but at enterprise scale, thousands of tests on every commit, the costs become significant.
Reserve agentic execution for tests where it adds real value: complex journeys, frequently changing UI, and new feature coverage. Let traditional scripts handle the stable, low-complexity core.
Agents Need Human Review, Always
The phrase "autonomous testing" can create a dangerous expectation: that you can set agents running and walk away. AI agents should operate with well-defined boundaries, escalation paths, and review checkpoints, not full autonomy over critical decisions.
The right mental model is "agents amplify QA engineers," not replace them. Agents handle volume work; humans handle strategy, triage, and judgment calls.
Compounding Errors in Multi-Step Workflows
Every step in an agent's execution carries a small error probability, and these compound. An agent that's 95% reliable per step is only about 60% reliable across ten sequential steps.
Mitigate with checkpointing: verify state at defined milestones (cart, payment, confirmation) rather than only checking the final output.
Skill Gap and Organizational Readiness
Agentic AI systems require a different kind of operational discipline than traditional software. Teams need to understand how to write effective goal specifications, how to review agent outputs, how to interpret failure analysis, and how to gradually expand agent autonomy as trust is established. Our agentic AI interview questions pressure-test that understanding, and TestMu AI certifications validate it.
Key Takeaways and Next Steps for Agentic AI
Agentic AI moves software from answering to acting. An agentic system takes a goal, plans the steps, acts through tools, and checks its own work in a loop, which makes it useful for multi-step work that used to need a person to orchestrate it. The same autonomy makes it non-deterministic, so it needs scoped use cases, human checkpoints, and evaluation that scores behavior rather than a single output.
To go further, our agentic AI frameworks comparison covers choosing a stack across nine frameworks, including LangGraph, CrewAI, AutoGen, and the OpenAI Agents SDK, and the testing your first AI agent guide covers evaluating the agents you build. For agents built on Google Cloud, the walkthrough on how to test a Vertex AI Agent Builder agent applies that evaluation step by step. For agents built on OpenAI's SDK, see how to test OpenAI Agents SDK agents.
The principles to carry forward: start small, keep a human in the loop for anything irreversible, and measure what the agent did, not only what it said. For the infrastructure side, read about scaling browser infrastructure for AI agents.
TestMu Conference 2026 also had a session on agent autonomy, Will the Real Autonomous Agent Please Stand Up.
Sources Cited in This Guide
- Gartner, Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027, press release, June 25, 2025, linked in the risks section above.
- Shu et al., Towards Effective GenAI Multi-Agent Collaboration: Design and Evaluation for Enterprise Applications, arXiv:2412.05449, linked in the architecture patterns section above.
- Yao et al., ReAct: Synergizing Reasoning and Acting in Language Models, arXiv:2210.03629, October 2022, linked in the architecture patterns section above.
- GitHub Docs, documentation for Copilot cloud agent; Klarna, Klarna AI assistant handles two-thirds of customer service chats in its first month, press release, February 27, 2024; Google Project Zero, From Naptime to Big Sleep, November 1, 2024. All three are linked in the use cases section above.
- CloudQA, Why Your Automation Suite Is Slowing You Down, a vendor estimate linked in the goal-based testing section above.
- KaneAI: World's First End-to-End Software Testing Agent, TestMu AI on YouTube.
- TestMu AI documentation, Getting Started With KaneAI, linked in the hands-on section above.
Author
Onwuemene Joshua
Blogs: 1
Onwuemene Joshua is a Documentation Writer with expertise in API and product documentation. He holds a Bachelor's degree in Engineering (BEng) and actively contributes as a freelancer and open-source documentation writer. He has 3+ years of hands-on experience using tools such as Docusaurus, Markdown, Swagger, and Git. His work includes leading and authoring end-to-end developer documentation for APIs, onboarding guides, and open-source projects. Joshua led a three member team during phase one of the Technical Writing Mentorship Program (TWMP) documentation migration from Hugo to Docusaurus. In this role, he reviewed pull requests and provided constructive feedback to contributors.
Reviewer
Himanshu Sheth
Reviewer
Himanshu Sheth is the Director of Marketing (Technical Content) at TestMu AI, with over 8 years of hands-on experience in Selenium, Cypress, and other test automation frameworks. He has authored more than 130 technical blogs for TestMu AI, covering software testing, automation strategy, and CI/CD. At TestMu AI, he leads the technical content efforts across blogs, YouTube, and social media, while closely collaborating with contributors to enhance content quality and product feedback loops. He has done his graduation with a B.E. in Computer Engineering from Mumbai University. Before TestMu AI, Himanshu led engineering teams in embedded software domains at companies like Samsung Research, Motorola, and NXP Semiconductors. He is a core member of DZone and has been a speaker at several unconferences focused on technical writing and software quality.
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