Coding agents are changing how we build software because they can hold a goal for longer than a human can tolerate. They can scan a repo, touch dozens of files, and keep iterating until something works. The trap is assuming that “more autonomy” means “less thinking.” In practice, the builders who get the most out of coding agents are the ones who design a tight harness around them. They give clear goals, verifiable signals, and a workflow that makes mistakes cheap.
If you are a solo founder or indie hacker building AI-first products, this matters even more. You are shipping with limited time, limited budget, and not a lot of tolerance for backend surprises. Agentic coding can feel like a superpower until you hit the first production-only bug, the first auth edge case, or the first runaway cloud bill.
This is the playbook we use internally. It is not about magic prompts. It is about repeatable agent planning patterns, context hygiene, and review loops that turn agentic workflows into something you can trust.
Understand the harness you are actually running
Every agent experience is a “harness,” whether you call it that or not. It is the combination of the instructions guiding the model, the tools it can use, and the messages you feed it. The practical takeaway is simple. Your results depend as much on the harness as on the model.
If an agent can search the codebase, run tests, and inspect diffs, you can give it higher-level goals and let it pull context as needed. If it cannot, you must provide more explicit context and narrower tasks. This is why “the same prompt” can behave differently across editors, models, and setups.
For solo builders, the mistake is trying to compensate with longer prompts. Long prompts often smuggle in contradictions. Instead, keep your harness strong. Make sure your agent can search, run your test command, run your lint command, and produce a clean diff. Then you can focus on product decisions.
A quick practical check. If you cannot describe your repo’s “definition of done” in two sentences, your agent cannot either. Get that into writing, even if it is just in your notes.
Right after you have that, it is worth removing backend uncertainty from the loop.
If you want your agent-generated frontend to talk to a real backend in the same afternoon, spin up a managed foundation like SashiDo - Backend for Modern Builders. We built it so Database, APIs, Auth, Storage, Functions, Realtime, Jobs, and Push are ready in minutes, not days.
Start with plans, not code
The biggest “agent unlock” is planning before generation. When you ask for a plan, you force a structure that agents can execute reliably. You also get a chance to catch misunderstandings before they become refactors.
Planning works because it turns a vague intention into a set of verifiable steps. It also prevents a common failure mode in coding with AI. The agent starts implementing the easiest interpretation of your request, and then you spend 20 turns trying to steer it back.
In practice, a good agent plan is specific about three things: what will change, where it will change, and how success will be verified.
What a good plan looks like in the real world
A plan is useful when it references concrete artifacts, not just outcomes. Instead of “add auth,” a plan should state which auth provider you want, which routes or screens change, and what test or manual check proves it works.
If you are building a small AI app, a plan might specify that you will add a user table, store provider tokens securely, and log model usage per user. The moment you say “per user,” your plan needs to mention where that user identity comes from and what happens on logout.
This is also where backend choice changes your agent success rate. When the backend primitives already exist, your plan becomes shorter and clearer.
With SashiDo - Backend for Modern Builders every app starts with a MongoDB database and a CRUD API, plus a full user management system with social logins. That makes it easier to plan in terms of objects, permissions, and flows. You can focus on product behavior instead of wiring.
When to restart from the plan
If the agent ships something “technically correct” but wrong for your intent, restarting from the plan is often faster than trying to patch. Revert the changes, tighten the plan, and rerun.
This is not wasted effort. It is how you build a library of patterns that work for your app. Over time, your plans become reusable templates for the next feature.
Context management: let the agent search, but keep the problem small
In agentic workflows, context is your budget. Every extra file, log, or irrelevant snippet competes with what actually matters. The best habit is to describe the problem in product terms and let the agent earn context through search.
That said, there are a few cases where you should provide context explicitly.
If there is a canonical example in your codebase, point to it. If there is a single file that defines your patterns, tag it. If there is a policy or constraint, state it once, clearly, and keep it stable.
The anti-pattern is dumping half your repo into the prompt. Agents will often anchor on the first thing they see and apply the wrong pattern everywhere.
When to start a new conversation
Agents degrade with long, meandering threads. After many turns, you get context noise. The agent starts “remembering” outdated decisions, or it tries to satisfy old constraints that no longer apply.
Start a new thread when you switch features, when the agent keeps repeating the same mistake, or when you have completed a clean unit of work. Continue in the same thread when you are iterating on the same feature and the last few messages contain important constraints.
This is also where solo founders save time. You can run short, single-purpose threads and treat them like tickets.
Build rules and skills that remove repeat mistakes
If you use coding agents daily, you will notice the same issues repeating. Commands are wrong. File locations drift. Code style breaks. Tests are skipped.
The fix is not “better prompting forever.” The fix is making project-level rules that are always present, and task-level skills or commands you can reuse.
Project rules should stay short. They should document your build and test commands, the few code style constraints that matter, and pointers to canonical files.
Skills or commands should package workflows you do repeatedly. For solo founders, a few high leverage ones are:
- A “ship checklist” command that runs typecheck, lint, tests, and builds a release bundle.
- A “new API endpoint” command that creates a route, adds input validation, adds tests, and updates docs.
- A “review” command that inspects diffs and flags risky changes in auth, billing, and data access.
The point is not automation for its own sake. The point is protecting your focus. When the agent can reliably execute the boring steps, you can spend brain cycles on product and UX.
Verifiable goals: tests, linters, and real constraints
Agents are strong at iteration when success is measurable. They are weak when success is “looks good to me.” That is why verifiable goals matter.
Tests are the obvious example. In empirical studies, TDD has been linked to improved design quality and maintainability outcomes, especially when teams use tests to drive structure rather than as an afterthought. Janzen and Saiedian’s work is a useful starting reference if you want the research angle, but the practical lesson is what matters. Tests force you to define behavior before implementation. That makes agents more reliable because they have a target to hit. See: Test-Driven Development: Concepts, Taxonomy, and Future Direction.
There is also a security reason to care. Multiple analyses show that AI-generated code can include vulnerabilities, and iterative refinement does not always reduce risk. In some settings it can even increase critical issues across iterations if you do not anchor on security checks. A recent analysis on “security degradation” during iterative generation is a good reminder to keep security verifiable, not assumed. See: Security Degradation in Iterative AI Code Generation.
A TDD-style workflow that works well with agents
A reliable agentic workflow is to separate test writing from implementation. The moment you mix them, agents will “helpfully” change tests to match their implementation, and you lose the signal.
A simple loop looks like this:
- First ask the agent to write tests based on explicit input and output pairs, and insist it does not write implementation.
- Run the tests and confirm they fail for the right reason.
- Commit the tests.
- Ask the agent to implement until the tests pass, and tell it not to modify the tests.
This is not slower. It is usually faster because it turns debugging into a deterministic loop.
Use backend primitives as your test harness
A big reason indie builders get stuck is that their “backend” is an ever-changing local stack. The agent writes code against an API that does not exist yet, or it assumes auth behavior that differs in production.
When you anchor the backend early, you give your agent stable interfaces. With SashiDo, you can plan against concrete primitives: a MongoDB-backed CRUD API, a built-in user system, file storage on S3 with CDN, cloud functions, realtime over WebSockets, scheduled jobs, and push notifications.
That changes what “verifiable” means. You can write tests and checks against real endpoints and real permissions, not mocked hopes.
If you want a fast path, our Getting Started Guide shows how we expect apps to be structured from day one. It is the kind of baseline that makes agent planning patterns much easier to repeat.
Reviewing agent output: treat diffs as the product
The fastest way to get burned by coding with AI is to trust code because it compiles. AI-generated code can be subtly wrong while still passing basic checks.
A good review process is not a moral stance. It is a practical safety system.
Review should happen in layers.
During generation, watch the direction. Interrupt early if the agent is editing the wrong files or introducing new abstractions you did not ask for. After generation, review the diff like you would review a PR. Look for permission changes, data model changes, and error handling.
For auth and session work, you should have a “red list” of things you always check. OWASP’s session management guidance is a good baseline. It covers fundamentals like using secure session identifiers, regenerating session IDs after login, setting cookie attributes like HttpOnly and Secure, and avoiding session tokens in URLs. See: OWASP Secure Coding Practices Checklist, Session Management.
For solo founders, the meta-lesson is to create review heuristics that are faster than full re-reading. If the agent touched anything in auth, billing, or permissions, slow down and review line-by-line.
Parallelism: use multiple agents without creating chaos
Parallel agents are one of the most underrated advantages of agentic workflows. You can ask two models to solve the same problem and compare. Or you can run one agent on tests while another drafts documentation.
The danger is stepping on your own changes. The clean solution is isolation.
Git worktrees are a pragmatic tool here because they let you have multiple working directories attached to the same repository. Each agent can operate in its own worktree and you can merge the best result back. If you want the definitive reference, Git’s documentation is the source of truth. See: git-worktree documentation.
A real-world pattern that works is to run parallel attempts only for tasks with clear acceptance criteria, like “make tests pass,” “remove this deprecation,” or “refactor this module without changing behavior.” For ambiguous design work, parallelism can produce divergent architectures that cost more time to reconcile.
Delegating: cloud agents are best for background chores
Cloud or long-running agents shine for tasks you would otherwise add to a todo list and procrastinate on. Writing missing tests, updating docs, cleaning up recent refactors, or chasing flaky tests.
The rule is to delegate things that have a clear finish line. “Improve performance” is not a clear finish line. “Reduce p95 response time for endpoint X by 20% and show before and after measurements” is.
If you are building on SashiDo, delegation gets easier because the operational surface area is smaller. We handle platform monitoring, SSL, hosting, and the managed backend pieces. Your agent tasks stay focused on application logic.
If you do hit scale issues, plan for it explicitly. We built “Engines” to scale compute and performance in controlled steps. Our write-up on how Engines work helps you decide when you actually need more power and how cost is calculated. See: Power up with SashiDo Engines.
Debug mode thinking: stop guessing, start instrumenting
When an agent gets stuck on a tricky bug, the worst thing you can do is encourage guess-fix loops. You end up with random changes that “might” work.
The better approach is hypothesis, instrumentation, reproduce, analyze, fix. Agents are surprisingly good at generating plausible hypotheses and adding targeted logs. They are also good at suggesting what runtime data would disambiguate two competing explanations.
The critical step you must supply is a crisp reproduction recipe. If you cannot reproduce it, the agent cannot either.
For example, if your bug is “users randomly get logged out,” do not ask the agent to “fix auth.” Ask it to list hypotheses, add instrumentation around token refresh and session expiration, and tell you what to capture in logs. Then reproduce with those logs enabled and rerun.
This is also where backend observability matters. When your auth, data, and functions are centralized, you can place instrumentation where it counts. If you spread logic across ad hoc services, you spend your weekend chasing ghosts.
Practical checklists for indie builders shipping with agents
You do not need a massive process. You need a few hard gates that prevent the most expensive failures.
Prompt checklist for agent planning patterns
Before you run the agent, make sure your request includes:
- The goal stated as an outcome, plus the verification method. For example, which tests must pass or which endpoint should return what.
- The boundaries. Which files or directories are in scope, and what should not be touched.
- The non-negotiables. Security constraints, performance constraints, and data retention rules.
Review checklist for agent-generated changes
When you review the diff, always scan for:
- Auth and permissions changes, especially default access, role checks, and session lifecycle.
- Data model migrations or schema-like changes in your objects.
- Error handling. Look for swallowed exceptions and ambiguous fallbacks.
- New dependencies. Especially anything that pulls in native modules or changes bundling.
If any of these are present, slow down. If none are present and tests pass, ship faster.
Where a managed backend makes agentic workflows less fragile
A lot of frustration with coding agents is not actually about the agent. It is about the moving ground underneath it.
When you are vibing your way through a prototype, the agent can create endpoints, auth flows, and background jobs quickly. The problem is that every one of those components has operational consequences. Who runs the database. Where do files go. How do you scale realtime. What happens when push notifications need to deliver at volume.
This is why we built SashiDo - Backend for Modern Builders. We wanted a backend that matches the reality of modern building. Fast prototyping, then real scaling, without a DevOps detour.
If you are evaluating alternatives like Firebase, the practical comparison is not just features. It is how easily your agentic workflows map onto real backend primitives, and how predictable your costs and scaling path feel when you go from weekend project to real traffic.
Pricing matters for solo founders, so keep it grounded. We keep a 10-day free trial with no credit card required, and our current plans and overage details are always kept up to date on our pricing page.
A helpful suggestion before you scale your next agent-built feature
If your agent can generate the frontend in a night, give it a backend it can rely on the next morning. You can explore SashiDo’s platform at SashiDo - Backend for Modern Builders and wire MongoDB, Auth, Functions, Realtime, Storage, Jobs, and Push without adding DevOps work.
Conclusion: make coding agents boringly reliable
The goal is not to “use a cursor agent” or any other tool perfectly. The goal is to make coding agents predictable enough that you can ship on a schedule. Plans reduce wasted work. Context hygiene keeps agents focused. Tests and linters give them measurable targets. Review turns fast output into trustworthy output. Parallel runs help when the task has clear acceptance criteria. Debug-mode thinking replaces guesswork with evidence.
If you want to build an agent-led prototype that does not collapse at the backend boundary, start by anchoring on stable primitives. We built SashiDo for exactly that.
Ready to prototype, deploy, and scale without DevOps? Start your 10-day free trial on https://www.sashido.io/en/ and get a production-ready backend running in minutes.
