Mahal Systems Overview › Agent type
Web research
Browsing the open web to answer or research.
Specific to web research
- 8.b.i
Tune the search tool: snippets, enough results, retries, reformulation, filters, source quality
Make the search tool return what the model needs to choose what to open (a snippet per result, not just titles; five or more results; a full-document reader beside truncated search), retry failed searches, and reformulate when a query returns little (start broad, then narrow; use the corpus's own terms) rather than retrying it verbatim.
- 9.a.ii
Verify citations claim by claim for factual support
Extract claim-citation pairs deterministically, then check each: the link resolves, the source is relevant, and the source actually supports the claim.
General optimizations with evidence from web research
- 1.b.i
Match the model and its effort to each call site and query class
Pick the model per role or call site rather than one model everywhere, and set its reasoning effort and sampling parameters per site; tune each role's prompt to its model (GEPA raised every model tested at lower 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.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.i
Parallel tool execution for read-only calls
Let the model put several independent read-only tool calls (searches, fetches, file reads) in one turn and have the harness run them at once, while calls that write, share state or need another call's result run one at a time in the order the model emitted them.
- 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.i
Detect stalls and loops: warn, then stop early
Track repeated identical actions (same tool, same arguments; same search query) and turns with no new information.
- 7.b.iii
Size run budgets so productive runs aren't cut off: turn caps, deadlines, command timeouts
Set the turn or step cap, the wall-clock deadline and per-command timeouts from what runs that succeeded actually needed, not from framework defaults, so a run that is still making progress is not stopped short.
- 7.d.ii
Inversion of control
Give the agent the goal, the tools and the acceptance checks and let it choose the sequence of steps, instead of coding a fixed workflow; the goal, permissions, budget and evaluation stay outside the agent.
- 7.d.iii
ReAct loop
Run the agent as a loop: the model reasons, calls a tool, reads the result and picks the next step, until it answers or hits a cap.
- 9.a.i
Feed back a sound external verifier's verdict; never the model's own critique
Run a check the model cannot fool on its output (compiler, tests, schema validation, execution, EXPLAIN on generated SQL, a solver) and feed the verdict back; a pass/fail verdict is enough, and resampling until the check accepts works about as well as detailed feedback.