Measure, improve, and prove your AI workflows.
Aegis connects production traces to evaluation, reinforcement learning, and durable memory so teams can improve agent behavior with measurable feedback instead of intuition.
Pull real traces, tool calls, and spans into a replayable operator loop.
Target weak dimensions with eval, tooling, RL, and memory instead of guesswork.
Show what changed, why it changed, and what held up on re-evaluation.
$ aegis pipeline strict-benchmark trace_ingest connected eval_depth 125 dimensions weakness_map generated environment_search hermes / nirofish reward_stack continuous memory_policy provenance-first
Production and staging behavior brought into one reviewable surface.
Rule checks, semantic signal, and judges only where they add real value.
Promotion stays provenance-first instead of becoming a blind vector dump.
Configs, manifests, and reports stay tied to each run for later review.
Versioned configs, pinned suites, and explicit evidence modes so shipping faster does not mean losing the audit trail.
Most AI workflows fail as systems long before they fail as models.
The problem is rarely just output quality. It is the missing loop between production behavior, structured evaluation, targeted intervention, and proof that the system actually improved.
Logs without replay
Teams see failures in traces, but lack a controlled way to replay them, score them, and compare interventions fairly.
Interventions without proof
Prompts, tools, and reward tweaks pile up quickly when there is no clean before-and-after contract for improvement.
Memory without lineage
Knowledge is easy to store and hard to trust unless it carries provenance, contradiction handling, and write policy.
One continuous loop, built to move from behavior to intervention.
Aegis is designed around the lifecycle that actually ships: bring behavior in, score weak dimensions, spin the right environments, train under explicit reward logic, retain what should persist, and measure again.
Trace ingestion
Bring spans, tool calls, and outputs from production or staging into replayable inputs.
Eval and weakness mapping
Score behavior across deterministic checks, semantic signals, and judges where they actually add signal.
Environment search
Spin targeted RL environments for weak parts of the workflow instead of training on generic noise.
Rewarded training
Run continuous reward stacks with inspectable assumptions and benchmark-aware guardrails.
Memory promotion
Persist what should survive with provenance, confidence, and reversible writes.
Re-evaluation
Measure the delta on held-out or frozen suites so lift is explicit instead of implied.
Bring real failures back into a controlled harness so the team can inspect the same thing, not argue from screenshots.
Generate environments around the failing dimension instead of broad, expensive retraining that muddies the signal.
Memory stays useful because writes remain explicit, inspectable, and tied back to their source behavior.
Held-out or pinned suites keep the loop honest and make the after-state visible to operators and stakeholders.
Built for the team shipping the workflow, not just the slide deck.
The product surface has to serve operators, researchers, and platform teams at the same time: command line where speed matters, UI where review matters, API where automation matters.
$ aegis eval benchmark --suite legal-heldout $ aegis train start --backend verl $ aegis memory inspect --agent policy:v2 artifacts/ manifest.json scores.json report.md replay_bank/
Operator workflows
Run strict benchmarks, launch training, and inspect artifacts without leaving the terminal.
Visual run inspection
Review eval runs, traces, rubrics, memory entries, and training jobs from one surface.
Automation-ready
Expose ingestion, evals, traces, and training orchestration through typed interfaces.
Auditable outputs
Keep configs, manifests, and score context together so results are reviewable later.
Benchmark integrity is part of the product, not a post-hoc slide.
Aegis separates evidence modes on purpose. Fast internal proxies, honest public proxies, and manifest-backed claim-grade paths are different products of rigor and should be represented that way.
Fast iteration loops
For regressions and ablations when the team needs high feedback velocity.
Honest external signals
Held-out and benchmark-native reporting without pretending every number is leaderboard proof.
Manifest-backed evidence
The strictest path: pinned suites, preserved artifacts, and reporting you can defend.
{
"suite": "claim-grade",
"trace_source": "production + replay",
"scoring": ["rule", "semantic", "judge"],
"memory_write_policy": "provenance_first",
"artifacts": ["manifest.json", "scores.json", "report.md"]
}Walk through your workflows, evaluation posture, and where the real leverage is.
We will be direct about what fits today, what still belongs on the roadmap, and what it takes to prove improvement instead of just implying it.