Mahal Systems Overview › Lever
Context
What enters and stays in the window: compaction, filtering, isolation, cache layout. 12 optimizations change this part of an agent.
- 2.a.i
Reduce the tool surface: remove and consolidate tools, then defer the rest behind search
Offer the agent only the tools the task needs.
- 2.c.i
Bound tool output: cap it, or write it to a file the agent can read
Cap the characters of tool output kept per call (VeRO settled near 15-20 KB on TerminalBench), or, better, write long output to a file and return its path with a short head, so the agent can read more if it needs to.
- 3.a.iii
Dynamic context injection
Put the runtime facts the model cannot know into its context each run: today's date and timezone, the working directory, OS and git state, the tools available, the user and locale.
- 3.b.i
Keep the prompt prefix cache-stable
Put static content (identity, rules, tool definitions) before a boundary and per-turn content (date, state, memory) after it; keep the prefix bit-for-bit identical across turns; strip thinking text from persisted history; and track per-turn cache-miss cost.
- 3.b.ii
Mask tools instead of removing them
When the tools an agent may use change mid-task (by mode, phase or permission), declare every tool once, keep the definitions byte-stable, and restrict the choice another way: OpenAI's allowed_tools or tool_choice "none", Anthropic's appended tool_removal and tool_addition blocks, a decode-time logit mask or grammar on self-hosted models, and a harness check that rejects calls outside the allowed set.
- 5.a.i
Compact conversation history in stages: elide stale tool outputs, then keep an anchored summary
Manage the history before it overflows.
- 5.b.i
Give the model the schema it queries, in context: descriptions, keys, codes, sample values
When the model writes SQL or structured filters, put the schema it is querying in its context: table and column descriptions, key relationships, what enum codes and nulls mean, sample values.
- 5.c.i
Discrete phase separation
Run research, planning and implementation as separate agent sessions, each starting from a fresh context that holds only the previous phase's written output (a research document, then a plan file), and have a person review that document before the next phase starts.
- 5.c.ii
Sub-agent architectures with clean contexts
Hand a context-heavy side task (code search, reading many files, documents or logs) to a sub-agent that starts with a fresh context and only the tools it needs, and have it return a short result or file references so the parent's context stays small.
- 6.b.iii
Use multi-agent orchestration only for breadth-first, independent sub-tasks
Spawn parallel sub-agents when the work splits into independent, breadth-first parts with little shared context; expect about 15x the tokens of a chat and up to 90% less wall-clock.
- 7.b.ii
Keep errors in context
Return every failed action to the model as an error it can read (the call, the cause, a capped trace) instead of swallowing the exception, retrying silently, returning an empty result or deleting the failed attempt, and collapse each error once it is resolved.
- 7.b.iv
Track explicit progress: a task list with a status per item, re-injected each turn
Give the agent a task-list tool (each item with a status, one in progress at a time), ask for the list on the first turn of non-trivial tasks, and re-inject the current list after the system prompt each turn instead of keeping old copies in history.