If you build with ai coding tools every day, you have probably noticed the same pattern we see across indie projects, hackathon demos, and early-stage startups. The model is smart, but your context is not. As soon as you connect your agent to a terminal, logs, APIs, or a handful of “helpful” tools, the conversation turns into a token furnace. Costs spike, responses get slower, and the agent starts missing obvious details because the prompt is stuffed with irrelevant noise.
The fix is rarely “use a bigger model” or “paste more info”. What works in practice is designing your workflow so the agent can discover the right context only when it needs it, instead of dragging everything into every step.
That approach is what we mean by dynamic context discovery. It is a simple pattern with big consequences. Lower token usage, more stable agent behavior, and fewer of those frustrating loops where your coding ai keeps re-reading the same wall of output.
After the first few wins with this pattern, most builders realize the next bottleneck is not the agent. It is the backend glue. Auth, data, files, push, jobs, deployment. That is exactly where we fit in with SashiDo - Backend for Modern Builders.
Need a quick path from idea to a working backend? Our Getting Started guide shows how to deploy auth, storage, and serverless functions in minutes.
Why context explodes in modern ai tool for code workflows
In a typical weekend build, your best ai coding assistant ends up juggling several streams of information: source files, terminal output, dependency errors, API responses, and sometimes third-party tool calls that return huge JSON payloads. Each stream is useful sometimes, but harmful most of the time.
The problem is structural. Context windows are finite. Models have a maximum token budget per request, and your prompt, tool outputs, and the model’s own response all compete for that space. When you hit the limit, you either truncate data, summarize aggressively, or lose crucial details. OpenAI’s docs and guides on context length and token budgeting are a practical reminder that “more context” has real cost and reliability implications, not just price implications. See the official guidance in the OpenAI developer documentation.
What we see in the field is that bloated context creates three failure modes:
- Token burn without progress. The agent spends tokens re-processing tool output that is not relevant to the current decision.
- Contradictory instructions. Long prompts often include outdated notes, previous attempts, and partial assumptions that confuse the agent.
- Brittle memory. When the agent hits the context limit and you summarize, it forgets the one detail that actually mattered.
Dynamic context discovery is a response to these failure modes. Instead of pushing everything into the prompt, you use a stable interface so the agent can pull what it needs, when it needs it.
Dynamic context discovery, explained like you actually ship software
The core move is to treat “context” as something addressable, not something you paste. In practice, that means you represent large, changing artifacts as files or file-like objects, and you give the agent lightweight ways to locate and load them.
This matters for ai for developers because it shifts the agent’s behavior from “react to whatever is in front of me” to “search, inspect, and confirm”. That is closer to how experienced builders debug. You do not read your entire log history every time. You skim the end, grep for what changed, then drill into a narrow slice.
The trade-off to accept up front
Dynamic discovery is not magic. It adds a small amount of friction because the agent has to do an extra step to fetch context. But you get a better overall trajectory because the agent is less likely to derail. You are trading a tiny lookup cost for fewer retries, fewer hallucinations, and fewer expensive full-context reruns.
Pattern 1: turn long tool responses into “readable later” files
Tool calls are where token budgets go to die. Shell commands, agent plugins, and third-party APIs can return enormous outputs. The common “solution” is truncation, but truncation is silent data loss. The missing line is often the one error message that matters.
A more stable pattern is to store the full tool output somewhere the agent can access, then only pull slices into the working context when needed. In a local workflow, this can literally be a file. In a platform workflow, it can be an object store, a database row, or a log artifact with a stable identifier.
When you implement this, you will notice two immediate improvements:
First, the agent stops re-reading the same long output in every step. Second, debugging becomes incremental. The agent can start with the last few lines, then widen the window only if the signal is not there.
This is also where backend choices start to matter. If you are building an agent that captures tool output, screenshots, or evaluation traces, you need cheap, scalable storage with predictable behavior. In our platform we back file storage with AWS S3 and a built-in CDN, which is a good fit for “store now, fetch later” artifacts. If you want the underlying concepts, AWS explains object storage clearly in its overview of Amazon S3 documentation.
Pattern 2: treat summarization as a reset, and keep history searchable
Summarization is a necessary evil. Once you hit the context window limit, you compress the conversation into a shorter form to keep going. But compression is lossy. The agent may “remember” an intent but lose a constraint, a version number, or a subtle edge case you agreed on 30 messages ago.
A simple fix is to keep chat history as a searchable artifact. Instead of hoping the summary contains everything, you make the full history retrievable. Then, if the agent realizes it needs the exact wording of a requirement or the full error trace, it can fetch it.
The key here is not philosophical. It is operational. Your agent becomes more self-correcting, because it can verify when it is unsure.
If you are prototyping an app where the agent is doing iterative work, this becomes incredibly practical. You can store history as structured data and correlate it with your deploys, tasks, and user sessions. Every SashiDo - Backend for Modern Builders app ships with a MongoDB database plus a CRUD API, which is a natural place to store these “runs” without spinning up separate infrastructure.
Pattern 3: skills and instructions work better when they are discoverable
Most teams eventually create “skills” for their agent. A checklist for code review, a playbook for migrations, or a policy for handling secrets. If you load every skill into every prompt, you get context bloat and conflicting guidance.
Instead, keep skills as files with:
- a short name and description that can be included in minimal static context
- the full instruction that the agent loads only when it decides it is relevant
This creates a clean separation. The agent starts with a small index of capabilities, then opts into a skill only when the task matches.
You see this pay off immediately with ai code review tools. The agent does not need the entire review rubric while it is scaffolding a feature. But the moment you switch to review mode, it can load the rubric and apply it consistently.
Pattern 4: load only the tools you need, especially with MCP and OAuth
As soon as you connect agents to secured systems, OAuth shows up. Logs, design files, vendor dashboards, analytics. That is where standards matter. OAuth 2.0 is defined in the IETF’s RFC 6749, and it explains why flows, tokens, and scopes exist in the first place.
Tool ecosystems that sit behind OAuth are powerful, but they create a new kind of context problem. Tool servers often expose lots of tools with long descriptions, and naive agent harnesses inject all of those descriptions up front.
A better pattern is to keep tool descriptions in an index the agent can browse. In practice, you give the model a minimal list of tool names, then let it load the full description only when it is about to use one. This is especially relevant with the Model Context Protocol, where tools are standardized and composable. The canonical reference is the Model Context Protocol specification.
The operational benefit is easy to measure. When you are not constantly paying token rent for unused tools, your agent runs become cheaper and more stable.
Pattern 5: treat terminal sessions and logs as files, not pastebins
Terminal output is the messiest kind of context because it is long, chronological, and noisy. Pasting it into chat is the fastest way to lose signal. Syncing it to a file-like store and letting the agent search within it is far more effective.
This also aligns with how real systems work. For anything realtime, you will likely use streaming logs, websockets, or pub-sub. WebSockets are standardized in the IETF’s RFC 6455, and they are a common fit for realtime dashboards and “live agent run” experiences.
When you treat terminal output as discoverable, you unlock practical workflows like:
- ask why a command failed, and let the agent focus only on the relevant segment
- correlate a deploy with a spike in errors without re-reading the whole session
- keep long-running processes understandable even after hours of output
How to apply dynamic context discovery in a weekend AI prototype
This is where things get real for the vibe coder. You are not trying to build a perfect agent platform. You are trying to ship something demoable, get feedback, and not wake up to a cloud bill you cannot explain.
The goal is a workflow where your best ai coding tools stay focused, and your backend quietly handles the boring parts.
A practical architecture that keeps tokens predictable
Start by deciding what should be “in prompt” versus “discoverable”. Keep the prompt for tight, stable instructions. Move everything large, volatile, or historical into retrievable artifacts.
In practice, this is a solid checklist:
- Keep your agent’s rules and constraints short and stable. Everything else should be referenced, not pasted.
- Store tool outputs, logs, and long API responses as artifacts with identifiers. The agent can fetch slices on demand.
- Persist conversation history in a way that is searchable. Summaries are useful, but the raw truth should stay accessible.
- Keep tool descriptions and skills indexed, so the agent loads only what it needs.
Once you do this, you typically see fewer “agent spirals” and fewer forced restarts.
Where SashiDo removes the backend friction
Dynamic context discovery reduces token spend and stabilizes trajectories. But your prototype still needs auth, storage, database, functions, and realtime. If you are doing this solo, building that stack from scratch is where weekends go to die.
With our platform, you can stand up a production-ready backend fast.
You start with a MongoDB database and instant CRUD APIs. Then you add user management with social logins, which is critical when you are demoing an AI feature and want users to sign in with minimal friction. You can store artifacts, tool outputs, prompt templates, and evaluation traces in our file storage, backed by S3 and delivered via CDN. When you need custom logic, you deploy JavaScript serverless functions close to your users in Europe or North America.
If your agent experience includes collaborative state, a shared scratchpad, or multi-device sync, realtime over WebSockets is a natural fit. For work that needs to happen out of band, like nightly evaluations or recurring maintenance, we also support scheduled and recurring jobs you can manage from our dashboard.
If you want the fastest path to a working app, our Parse Platform docs and guides are where most builders start.
Token savings are great. Backend cost predictability matters too.
Indie builders worry about token costs for good reason. But what often kills a prototype is backend unpredictability. A sudden jump in requests, unexpected file transfer, or an expensive database scaling choice.
That is why we are explicit about pricing. We offer a 10-day free trial with no credit card required, and plans start at a low monthly price per app. Because pricing can change over time, always check the current numbers on our pricing page.
When you need to scale, do it deliberately. Our Engines let you tune performance and compute without rebuilding your architecture. The practical overview is in our deep dive on SashiDo Engines.
If you are coming from Firebase and you are comparing trade-offs like vendor lock-in, query patterns, and predictable scaling, our SashiDo vs Firebase comparison is the most direct starting point.
Common failure modes, and how to avoid them
Dynamic context discovery can still fail if the system around it is sloppy. These are the issues we see most often.
Over-indexing everything, then never retrieving it
If you store every artifact but do not give the agent a clean way to locate it, you have just moved the mess from the prompt to a folder. The fix is to keep a small, consistent index. Think of it as an address book. Artifact names, timestamps, and short descriptions.
Summaries that become “truth”
Summaries are helpful, but they should not be authoritative. Treat them as a convenience layer, not the source of truth. Keep raw logs and history accessible so the agent can verify.
Tool bloat sneaking back in
Tool ecosystems grow. The moment you add “just one more integration,” you risk reintroducing huge tool descriptions and unused capabilities. Keep tools discoverable and load them only when called.
Pushing realtime too early
Realtime is powerful, but it adds complexity. If your prototype is mostly single-user, start with a simpler polling model and add realtime when the product needs it. When you do need it, make sure the transport is robust and understood. WebSockets are standard for a reason.
Further reading (so you can verify the details)
If you want to go deeper on the underlying primitives that show up in agent and backend workflows, these are the canonical references we recommend.
- OpenAI developer documentation. Useful for understanding token budgeting, context limits, and tool patterns.
- Model Context Protocol specification. The emerging standard for tool and context interoperability.
- RFC 6749: OAuth 2.0 Authorization Framework. The baseline for secure access to external services.
- RFC 6455: The WebSocket Protocol. The standard reference for realtime connections.
- Amazon S3 documentation overview. A good grounding in object storage concepts.
Conclusion: use ai coding tools without paying the context tax
Dynamic context discovery is one of those rare patterns that helps immediately. It lowers token usage, reduces confusion, and makes agent runs more resilient over long trajectories. The key is to stop treating context like a pastebin and start treating it like an indexed system the agent can explore.
If you do that, your ai coding tools become more predictable. Then you can focus on what actually differentiates your product. The UX, the data, and the behaviors that users care about.
If you are ready to stop rebuilding the same backend pieces for every prototype, you can explore SashiDo’s platform and deploy database, auth, storage, functions, realtime, and jobs in minutes.
When you are ready, start a free 10-day trial and ship your AI prototype on SashiDo - Backend for Modern Builders. You will get MongoDB with CRUD APIs, user auth with social logins, file storage with CDN, serverless functions, realtime, jobs, and push notifications without DevOps.
