How to build agents with filesystems and bash

Link to headingHow agents read filesystems

The agent runs in a sandbox with your data structured as files. When it needs context, it explores the filesystem using Unix commands, pulls in what's relevant, and sends that to the LLM.

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Agent receives task
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    ↓
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Explores filesystem (ls, find)
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    ↓
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Searches for relevant content (grep, cat)
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    ↓
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Sends context + request to LLM
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    ↓
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Returns structured output

The agent and its tool execution run on separate compute. You trust the agent's reasoning, but the sandbox isolates what it can actually do.

Link to headingWhy filesystems work for context management

The typical approach to agent context is either stuffing everything into the prompt or using vector search. Prompt stuffing hits token limits. Vector search works for semantic similarity but returns imprecise results when you need a specific value from structured data.

Filesystems offer a different tradeoff.

Structure matches your domain. Customer records, ticket history, CRM data. These have natural hierarchies that map directly to directories. You're not flattening relationships into embeddings.

Retrieval is precise. grep -r "pricing objection" transcripts/ returns exact matches. When you need one specific value, you get that value.

Context stays minimal. The agent loads files on demand. A large transcript doesn't go into the prompt upfront. The agent reads the metadata, greps for relevant sections, then pulls only what it needs.

Link to headingMapping your domain to files

Let's look at some concrete examples of how different domains map to filesystem structures.

Example 1: Customer support system

Instead of throwing raw JSON into your agent, structure it:

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/customers/
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  /cust_12345/
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    profile.json          # High-level info
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    tickets/
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      ticket_001.md       # Each ticket
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      ticket_002.md
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    conversations/
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      2024-01-15.txt      # Daily conversation logs
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    preferences.json

When a customer asks "What was the resolution to my issue?", the agent can ls the tickets directory, grep for "resolved", and read only the relevant file.

Example 2: Document analysis system

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/documents/
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  /uploaded/
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    contract_abc123.pdf
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    invoice_def456.pdf
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  /extracted/
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    contract_abc123.txt
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    invoice_def456.txt
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  /analysis/
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    contract_abc123/
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      summary.md
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      key_terms.json
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      risk_assessment.md
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/templates/
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  contract_analysis_prompt.md
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  invoice_validation_rules.md

Raw inputs in one place, processed outputs in structured directories. The agent can reference previous analysis without reprocessing.

Link to headingCase study: Sales call summary agent

We built a sales call summary template using this architecture. The agent analyzes sales call transcripts and generates structured summaries with objections, action items, and insights.

The agent sees this file structure:

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gong-calls/
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  demo-call-001-companyname-product-demo.md     # Current call transcript
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  metadata.json                                 # Call metadata
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  previous-calls/
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    demo-call-000-discovery-call.md             # Prior discovery call
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    demo-call-intro-initial-call.md             # Initial intro call
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salesforce/
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  account.md                                    # CRM account record
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  opportunity.md                                # Deal/opportunity details
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  contacts.md                                   # Contact profiles
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slack/
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  slack-channel.md                              # Slack history
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research/
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  company-research.md                           # Company background
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  competitive-intel.md                          # Competitor analysis
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playbooks/
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  sales-playbook.md                             # Internal sales playbook
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The agent explores this like a codebase:

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# Explore what's available
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$ ls sales-calls/
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customer-call-123456-q4.md
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metadata.json
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# Read the metadata
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$ cat sales-calls/metadata.json
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# Look for objections
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$ grep -i "concern\|worried\|issue\|problem" sales-calls/*.md

The intuition is that the agent treats the transcript like a codebase. It searches for patterns, reads sections, and builds context just like it would debug code. No custom retrieval logic. The agent decides what context it needs using tools it already knows how to use. It handles edge cases we never anticipated because it's working with the raw information, not parameters we defined.

We'll have another post diving deeper into the sales call summary agent.

Link to headingWhy you should use bash and filesystems

Native model capabilities. grep, cat, find, awk. These aren't new skills we're teaching. LLMs have seen these tools billions of times during training. They're native operations, not bolted on behaviors.

Future-proof architecture. As models get better at coding, your agent gets better. Every improvement in code understanding translates directly. You're leveraging the training distribution instead of fighting against it.

Debuggability. When the agent fails, you see exactly what files it read and what commands it ran. The execution path is visible. No black box.

Security through isolation. The sandbox lets the agent explore files without access to production systems. You trust the reasoning, not the execution environment.

Less code to maintain. Instead of building retrieval pipelines for each data type, you write files to a directory structure. The agent handles the rest.

Link to headingGet started

Every agent needs filesystem and bash. If you're building an agent, resist the urge to create custom tools. Instead, ask: can I represent this as files?

We recently open-sourced bash-tool, a dedicated tool that powers this pattern.

1. AI SDK for tool execution and model calls

2. bash-tool for sandboxed filesystem access

3. Sales Call Summary template to see the full pattern and to get started with one-click

The future of agents might be surprisingly simple. Maybe the best architecture is almost no architecture at all. Just filesystems and bash.