Agentic AI: How to Save on Tokens

The secret of building production grade autonomous agents is that they are absolute token incinerators.

In a standard chatbot architecture, one user request equals one API call. In an agentic workflow, one user request can quietly trigger a multi step loop of 10, 20, or 50 model calls as the agent reasons, invokes tools, analyzes errors, and retries. Because LLM APIs are stateless, every single step resends the entire history, system prompt, and tool schema.

If you don't optimize, a single debugging or automation session can easily scale your costs by 10x to 100x. GitHub recently tackled this head-on, sharing how they slashed their own internal agent token spend by up to 62%.

Here is how you can implement a "Tokenmaxxing" strategy to stop wasting money without degrading your agent's intelligence.

1. Aggressively Prune Your Tool Manifests

The Model Context Protocol (MCP) and native function-calling are incredible, but they come with a heavy tax. Every time you expose a tool to an agent, you are injecting its JSON parameters and text descriptions into the system prompt.

If your agent has access to an MCP server or utility library with 30 distinct tools, it's carrying roughly 3,000 to 5,000 tokens of schema on every single turn, even if it only uses one tool.

Implement Lazy Loading / Routing: Don't give every agent every tool. Use a cheap router model to analyze the user's intent first, and then only inject the tool schemas relevant to that specific task domain (e.g., if the user asks about a database, drop the email and weather tools from the context).
Compress Manifests: Write precise, hyper-dense tool descriptions. Use shorter parameter names (q instead of search_query_string) and strip out optional fields from the base schema definitions.

2. Enforce Strict Context Compaction (Pruning)

As an agent cycles through a loop, the context window accumulates tool execution outputs, stack traces, and reasoning paths. If an agent reads a 2,000-line log file to fix a bug, that log file gets re-sent to the API over and over again on steps 3, 4, and 5.

[Step 1 Input] -> System Prompt + Tool Schema + User Query
[Step 2 Input] -> System Prompt + Tool Schema + User Query + Step 1 Output + Tool Result
[Step 3 Input] -> System Prompt + Tool Schema + User Query + Step 1 & 2 Output + Tool Results (Snowball Effect!)

Summarize the Past: Implement an automated compaction step. Instead of appending raw tool results verbatim, have a cheaper model summarize previous tool responses into highly dense key-value pairs (e.g., User Preference: Budget < $1000, OS: Linux).
Filter at the Server Level: If an agent queries a database or a GitHub PR API, don't let the server return the entire JSON payload. Use server-side projection to strip away metadata, timestamps, and author profiles, returning only the exact lines or values the agent asked for.

3. Maximize Native Prompt Caching

Major LLM providers (including Anthropic, OpenAI, and DeepSeek) offer Prompt Caching. This allows the provider to cache static parts of your prompt on their servers, charging you up to 90% less for cache hits.

Because agent loops resend the exact same system instructions, tool definitions, and early conversation history repeatedly, they are the absolute perfect use case for caching.

Structure Wisely: Place your most static content (System Prompt -> Tool Definitions -> Core Data Documents) at the very beginning of the payload. Put the fast changing variables (the latest tool output or user message) at the very end. If a single token changes at the beginning of a text block, the entire subsequent cache invalidates.

4. Implement Model Cascading and Tiered Routing

Not every step in an agentic workflow requires an expensive "frontier" model like GPT-4o or Claude 3.5 Sonnet.

                       [ Incoming Task ]
                              │
                              ▼
                ( Router: Light & Cheap Model )
                 /            │             \
                /             │              \
      [ Simple Request ] [ Medium Complexity ] [ High-Level Reasoning ]
              │               │                       │
              ▼               ▼                       ▼
       ( Flash Model )  ( Haiku Tier )         ( Frontier Model )

Use a tiered routing architecture:

The Grunt Work (Flash / Haiku models): Use fast, sub dollar per million token models for syntax parsing, formatting raw tool inputs, routing, or summarizing log outputs.
The Brain Work (Frontier models): Reserve your expensive reasoning engines exclusively for the abstract steps like initial planning, evaluating final success, or writing complex code blocks.

5. Put Hard Governance Guardrails in Production

Runaway loops happen. If an agent gets stuck in an error correction loop (e.g., trying to execute a command, failing, and trying the exact same failing command again), it can burn through hundreds of dollars in minutes.

Never deploy an autonomous agent without hard boundaries:

The Step Cap: Set a strict limit on the maximum number of turns an agent can take (e.g., max_loops = 30). If it doesn't solve the issue by then, force a hard exit and escalate to a human.
Semantic Caching: Before hitting the LLM, check a vector database cache to see if the exact same sub task or tool failure has already been evaluated and resolved earlier in the cluster.
Token Budgeting: Track token consumption dynamically during the runtime execution. If a single workflow run crosses a threshold (like $2.00), temporarily throttle the model down to a cheaper tier or pause for human approval.

Take Control of Your Agent Costs Today

Stop letting autonomous loops drain your AI budget. Building efficient AI doesn't mean compromising on intelligence, it means building smarter architectures.

Ready to optimize your production workflows? Share these optimisation principles with your engineering team, and audit your agent's tool manifests before your next deployment!