Unit 6 · Solve long and complex tasks

Work through large tasks one step at a time

You let the actor run one observable step, inspect compact evidence, and continue from live state. This avoids stuffing a large task into one prompt or generated script.

agent()9 focused minutesNot started

Unit example (nearest native match)

See the idea in context

auto investigator = axllm::agent(signature, axllm::object({
    {"contextFields", axllm::array({"logs"})},
    {"contextPolicy", axllm::object({{"preset", "lean"}, {"budget", "balanced"}})},
    {"runtime", axllm::object({{"language", "JavaScript"}})},
}));

Filter inside the runtime
The full records stay available to code instead of being repeated in a prompt.
Expose compact evidence
Logging only the count gives the next turn a useful observation.
Continue from live values
Later turns can reuse matches without recomputing or replaying the dataset.

Run itIn your own project

include(FetchContent)
FetchContent_Declare(axllm GIT_REPOSITORY https://github.com/ax-llm/ax GIT_TAG main SOURCE_SUBDIR packages/cpp)
FetchContent_MakeAvailable(axllm)
target_link_libraries(your_app PRIVATE axllm::axllm)

#include <axllm/axllm.hpp>

auto llm = axllm::ai("openai", axllm::object({{"apiKey", std::getenv("OPENAI_API_KEY")}}));
auto classify = axllm::ax("review:string -> sentiment:class \"positive, negative, neutral\"");

Set OPENAI_APIKEY in your environment before running provider-backed code.

In the ax repo

From a clone of the ax repo:

npm run example -- cpp src/examples/cpp/long-agents/incident_log_forensics.cpp

Active practice

Show that you can use it

Answer 2 in a row to learn this · attempt 1

Keep exploring

See the idea in context

Show that you can use it

Source-backed follow-up